diff --git a/.docker/.dockerignore b/.docker/.dockerignore
deleted file mode 100644
index 7c119fc..0000000
--- a/.docker/.dockerignore
+++ /dev/null
@@ -1 +0,0 @@
-bonus_superlinked_rag/server
diff --git a/.docker/Dockerfile.data_cdc b/.docker/Dockerfile.data_cdc
deleted file mode 100644
index 1b96726..0000000
--- a/.docker/Dockerfile.data_cdc
+++ /dev/null
@@ -1,42 +0,0 @@
-# Use an official Python runtime as a parent image
-FROM python:3.11-slim
-
-ENV POETRY_VERSION=1.8.3
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    gcc \
-    python3-dev \
-    curl \
-    build-essential \
-    && apt-get clean \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install Poetry using pip and clear cache
-RUN pip install --no-cache-dir "poetry==$POETRY_VERSION"
-RUN poetry config installer.max-workers 20
-
-# Set the working directory
-WORKDIR /app
-
-# Copy the pyproject.toml and poetry.lock files from the root directory
-COPY ./pyproject.toml ./poetry.lock ./
-
-# Install the dependencies and clear cache
-RUN poetry config virtualenvs.create false && \
-    poetry install --no-root --no-interaction --no-cache && \
-    rm -rf ~/.cache/pypoetry/cache/ && \
-    rm -rf ~/.cache/pypoetry/artifacts/
-
-# Install dependencies
-RUN poetry install --no-root
-
-# Copy the data_cdc and core directories
-COPY ./src/data_cdc ./data_cdc
-COPY ./src/core ./core
-
-# Set the PYTHONPATH environment variable
-ENV PYTHONPATH=/app
-
-# Command to run the script
-CMD poetry run python /app/data_cdc/cdc.py && tail -f /dev/null
\ No newline at end of file
diff --git a/.docker/Dockerfile.feature_pipeline b/.docker/Dockerfile.feature_pipeline
deleted file mode 100644
index 96e6824..0000000
--- a/.docker/Dockerfile.feature_pipeline
+++ /dev/null
@@ -1,50 +0,0 @@
-# Use an official Python runtime as a parent image
-FROM python:3.11-slim-bullseye
-
-ENV WORKSPACE_ROOT=/usr/src/app \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    POETRY_HOME="/opt/poetry" \
-    POETRY_NO_INTERACTION=1 \
-    POETRY_VERSION=1.8.3
-
-RUN mkdir -p $WORKSPACE_ROOT
-
-# Install system dependencies
-RUN apt-get update -y \
-    && apt-get install -y --no-install-recommends build-essential \
-    gcc \
-    python3-dev \
-    curl \
-    build-essential \
-    && apt-get clean
-
-# Install Poetry using pip and clear cache
-RUN pip install --no-cache-dir "poetry==$POETRY_VERSION"
-RUN poetry config installer.max-workers 20
-
-RUN apt-get remove -y curl
-
-# Copy the pyproject.toml and poetry.lock files from the root directory
-COPY ./pyproject.toml ./poetry.lock ./
-
-# Install the dependencies and clear cache
-RUN poetry config virtualenvs.create false && \
-    poetry install --no-root --no-interaction --no-cache && \
-    rm -rf ~/.cache/pypoetry/cache/ && \
-    rm -rf ~/.cache/pypoetry/artifacts/
-
-# Set the working directory
-WORKDIR $WORKSPACE_ROOT
-
-# Copy the feature pipeline and any other necessary directories
-COPY ./src/feature_pipeline .
-COPY ./src/core ./core
-
-# Set the PYTHONPATH environment variable
-ENV PYTHONPATH=/usr/src/app
-
-RUN chmod +x /usr/src/app/scripts/bytewax_entrypoint.sh
-
-# Command to run the Bytewax pipeline script
-CMD ["/usr/src/app/scripts/bytewax_entrypoint.sh"]
diff --git a/.docker/Dockerfile.feature_pipeline.superlinked b/.docker/Dockerfile.feature_pipeline.superlinked
deleted file mode 100644
index c4757cc..0000000
--- a/.docker/Dockerfile.feature_pipeline.superlinked
+++ /dev/null
@@ -1,50 +0,0 @@
-# Use an official Python runtime as a parent image
-FROM python:3.11-slim-bullseye
-
-ENV WORKSPACE_ROOT=/usr/src/app \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1 \
-    POETRY_HOME="/opt/poetry" \
-    POETRY_NO_INTERACTION=1 \
-    POETRY_VERSION=1.8.3
-
-RUN mkdir -p $WORKSPACE_ROOT
-
-# Install system dependencies
-RUN apt-get update -y \
-    && apt-get install -y --no-install-recommends build-essential \
-    gcc \
-    python3-dev \
-    curl \
-    build-essential \
-    && apt-get clean
-
-# Install Poetry using pip and clear cache
-RUN pip install --no-cache-dir "poetry==$POETRY_VERSION"
-RUN poetry config installer.max-workers 20
-
-RUN apt-get remove -y curl
-
-# Copy the pyproject.toml and poetry.lock files from the root directory
-COPY ./pyproject.toml ./poetry.lock ./
-
-# Install the dependencies and clear cache
-RUN poetry config virtualenvs.create false && \
-    poetry install --no-root --no-interaction --no-cache && \
-    rm -rf ~/.cache/pypoetry/cache/ && \
-    rm -rf ~/.cache/pypoetry/artifacts/
-
-# Set the working directory
-WORKDIR $WORKSPACE_ROOT
-
-# Copy the 3-feature-pipeline and any other necessary directories
-COPY ./src/bonus_superlinked_rag .
-COPY ./src/core ./core
-
-# Set the PYTHONPATH environment variable
-ENV PYTHONPATH=/usr/src/app
-
-RUN chmod +x /usr/src/app/scripts/bytewax_entrypoint.sh
-
-# Command to run the Bytewax pipeline script
-CMD ["/usr/src/app/scripts/bytewax_entrypoint.sh"]
diff --git a/.env.example b/.env.example
deleted file mode 100644
index 04b8570..0000000
--- a/.env.example
+++ /dev/null
@@ -1,26 +0,0 @@
-
-# --- Required settings even when working locally. ---
-
-# OpenAI API config
-OPENAI_MODEL_ID=gpt-4o-mini
-OPENAI_API_KEY=
-
-# Huggingface API config
-HUGGINGFACE_ACCESS_TOKEN=
-
-# Comet ML (during training and inference) 
-COMET_API_KEY=
-COMET_WORKSPACE=  # such as your Comet username
-
-# --- Required settings ONLY when using Qdrant Cloud and AWS SageMaker ---
-
-# Qdrant cloud vector database connection config
-USE_QDRANT_CLOUD=false
-QDRANT_CLOUD_URL=
-QDRANT_APIKEY=
-
-# AWS authentication config
-AWS_ARN_ROLE=
-AWS_REGION=eu-central-1
-AWS_ACCESS_KEY=
-AWS_SECRET_KEY=
diff --git a/.github/ISSUE_TEMPLATE/clarify-concept.md b/.github/ISSUE_TEMPLATE/clarify-concept.md
deleted file mode 100644
index 2075c90..0000000
--- a/.github/ISSUE_TEMPLATE/clarify-concept.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Clarify concept
-about: Clarify concept
-title: Clarify concept
-labels: ''
-assignees: ''
-
----
-
-Ask anything about the course.
diff --git a/.github/ISSUE_TEMPLATE/questions-about-the-course-material.md b/.github/ISSUE_TEMPLATE/questions-about-the-course-material.md
deleted file mode 100644
index 55b60c1..0000000
--- a/.github/ISSUE_TEMPLATE/questions-about-the-course-material.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-name: Questions about the course material
-about: Questions about the course material
-title: Questions about the course material
-labels: ''
-assignees: ''
-
----
-
-Ask anything about the course material.
diff --git a/.github/ISSUE_TEMPLATE/technical-troubleshooting-or-bugs.md b/.github/ISSUE_TEMPLATE/technical-troubleshooting-or-bugs.md
deleted file mode 100644
index cb66ac0..0000000
--- a/.github/ISSUE_TEMPLATE/technical-troubleshooting-or-bugs.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-name: Technical troubleshooting or bugs
-about: Technical troubleshooting or bugs
-title: Technical troubleshooting or bugs
-labels: ''
-assignees: ''
-
----
-
-**Describe the bug or technical issue**
-A clear and concise description of what the bug is.
-
-**To Reproduce**
-Steps to reproduce the behavior:
-1. Go to '...'
-2. Click on '....'
-3. Scroll down to '....'
-4. See error
-
-**Expected behavior**
-A clear and concise description of what you expected to happen.
-
-**Screenshots**
-If applicable, add screenshots to help explain your problem.
-
-**Desktop (please complete the following information):**
- - OS: [e.g. iOS]
- - Browser [e.g. chrome, safari]
- - Version [e.g. 22]
-
-**Smartphone (please complete the following information):**
- - Device: [e.g. iPhone6]
- - OS: [e.g. iOS8.1]
- - Browser [e.g. stock browser, safari]
- - Version [e.g. 22]
-
-**Additional context**
-Add any other context about the problem here.
diff --git a/.gitignore b/.gitignore
index b93fe4c..ea175e5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -149,6 +149,8 @@ dmypy.json
 # pytype static type analyzer
 .pytype/
 
+.idea
+
 # Cython debug symbols
 cython_debug/
 
@@ -161,19 +163,3 @@ cython_debug/
 
 # Ruff
 .ruff_cache
-
-dataset/
-
-# Data
-output
-generated_dataset
-finetuning_dataset
-.cache
-.gradio
-training_pipeline_output
-mistral_instruct_generation
-cache
-rabbitmq
-
-# Credentials
-sagemaker_execution_role.json
\ No newline at end of file
diff --git a/INSTALL_AND_USAGE.md b/INSTALL_AND_USAGE.md
deleted file mode 100644
index b331e85..0000000
--- a/INSTALL_AND_USAGE.md
+++ /dev/null
@@ -1,392 +0,0 @@
-<table style="border-collapse: collapse; border: none;">
-  <tr style="border: none;">
-    <td width="20%" style="border: none;">
-      <a href="https://decodingml.substack.com/" aria-label="Decoding ML">
-        <img src="https://github.com/user-attachments/assets/f2f2f9c0-54b7-4ae3-bf8d-23a359c86982" alt="Decoding ML Logo" width="150"/>
-      </a>
-    </td>
-    <td width="80%" style="border: none;">
-      <div>
-        <h2>📬 Stay Updated</h2>
-        <p><b><a href="https://decodingml.substack.com/">Join Decoding ML</a></b> for proven content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications. Every week, straight to your inbox.</p>
-      </div>
-    </td>
-  </tr>
-</table>
-
-<p align="center">
-  <a href="https://decodingml.substack.com/">
-    <img src="https://img.shields.io/static/v1?label&logo=substack&message=Subscribe%20Now&style=for-the-badge&color=black&scale=2" alt="Subscribe Now" height="40">
-  </a>
-</p>
-
-------
-
-# Install 
-
-## Local dependencies
-
-Before starting to install the LLM Twin project, make sure you have installed the following dependencies on your local system:
-
-| Tool | Version | Purpose | Installation Link |
-|------|---------|---------|------------------|
-| Python | 3.11 | Runtime environment | [Download](https://www.python.org/downloads/) |
-| Poetry | ≥1.8.4 | Package management | [Install Guide](https://python-poetry.org/docs/) |
-| GNU Make | ≥3.81 | Build automation | [Install Guide](https://www.gnu.org/software/make/) |
-| Docker | ≥27.0.3 | Containerization | [Install Guide](https://www.docker.com/) |
-| AWS CLI | ≥2.18.5 | Cloud management | [Install Guide](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) |
-
-## Cloud services
-
-The code also uses and depends on the following cloud services. For now, you don't have to do anything. We will guide you in the installation and deployment sections on how to use them:
-
-| Service | Purpose | Cost | Mandatory Credentials |
-|---------|---------|---------|---------|
-| [HuggingFace](https://huggingface.com/) | Model registry | Free | `HUGGINGFACE_ACCESS_TOKEN` |
-| [Comet ML](https://www.comet.com/site/) | Experiment tracker | Free tier available | `COMET_API_KEY` <br> `COMET_WORKSPACE` | 
-| [Opik](https://www.comet.com/site/products/opik/) | Prompt monitoring & LLM Evaluation | Free tier available | `COMET_API_KEY` <br> `COMET_WORKSPACE` |
-| [OpenAI API](https://openai.com/index/openai-api/) | LLM API for recommender system | Pay-per-use | `OPENAI_API_KEY` | [Quick Start Guide](https://platform.openai.com/docs/quickstart) |
-| [MongoDB](https://www.mongodb.com/) | NoSQL database | Free tier available | - |
-| [Qdrant](https://qdrant.tech/) | Vector database | Free tier available | - |
-| [AWS](https://aws.amazon.com/) | Compute and storage | Pay-per-use | `AWS_ARN_ROLE` <br> `AWS_REGION` <br> `AWS_ACCESS_KEY` <br> `AWS_SECRET_KEY` |
-
-> [!IMPORTANT]
-> Also, you must manually ask for access on Hugging Face for the gated LLM `meta-llama/Llama-3.1-8B` we use throughout the course. It's free. [Do it here](https://huggingface.co/meta-llama/Llama-3.1-8B).
-
-
-## Supported commands
-
-We will use `GNU Make` to install and run our application.
-
-To see all our supported commands, run the following:
-```shell
-make help
-```
-
-## Configure
-
-All the sensitive credentials are placed in a `.env` file that will always sit at the root of your directory, at the same level with the `.env.example` file.
-
-Go to the root of the repository and copy our `.env.example` file as follows:
-```shell
-cp .env.example .env
-```
-Now fill it with your credentials, following the suggestions from the next section.
-
-### Getting credentials for cloud services
-
-Now, let's understand how to fill in all the essential variables within the `.env` file to get you started. The following are the mandatory settings we must complete when working locally:
-
-#### OpenAI
-
-To authenticate to OpenAI's API, you must fill out the `OPENAI_API_KEY` env var with an authentication token.
-
-```env
-OPENAI_API_KEY=your_api_key_here
-```
-
-→ Check out this [tutorial](https://platform.openai.com/docs/quickstart) to learn how to provide one from OpenAI.
-
-#### Hugging Face
-
-To authenticate to Hugging Face, you must fill out the `HUGGINGFACE_ACCESS_TOKEN` env var with an authentication token.
-
-```env
-HUGGINGFACE_ACCESS_TOKEN=your_token_here
-```
-
-→ Check out this [tutorial](https://huggingface.co/docs/hub/en/security-tokens) to learn how to provide one from Hugging Face.
-
-#### Comet ML & Opik
-
-To authenticate to Comet ML (required only during training) and Opik (uses the same Comet credentials), you must fill out the `COMET_API_KEY` and `COMET_WORKSPACE` env vars with your authentication token and workspace name.
-
-```env
-COMET_API_KEY=your_api_key_here
-COMET_WORKSPACE=your_workspace_name_here
-```
-
-→ Check out this [tutorial](https://www.comet.com/docs/v2/api-and-sdk/rest-api/overview/) to learn how to get the Comet ML variables from above. You can also access Opik's dashboard using 🔗[this link](https://www.comet.com/opik).
-
-#### AWS
-
-Required only for fine-tuning and inference, which we will show how to set up later in the document.
-
-#### Qdrant
-
-Optional, only if you want to use Qdrant cloud. Otherwise, you can complete the course using the local version of Qdrant.
-
-## Install local dependencies
-
-You can create a Python virtual environment and install all the necessary dependencies using Poetry, by running:
-```shell
-make install
-```
-> [!IMPORTANT] 
-> You need Python 3.11 installed! You can either install it globally or install [pyenv](https://github.com/pyenv/pyenv) to manage multiple Python dependencies. The `.python-version` file will signal to `pyenv` what Python version it needs to use in this particular project.
-
-After installing the dependencies into the Poetry virtual environment, you can activate your virtual environment into your current CLI by running:
-```bash
-poetry shell
-```
-
-## Set up the data infrastructure
-
-We support running the entire data infrastructure (crawling, CDC, MongoDB, and Qdrant) through Docker. Thus, with a few commands you can quickly populate the data warehouse and vector DB with relevant data to test out the RAG, training, and inference parts of the course.
-
-### Spin up the infrastructure
-
-You can start all the required Docker containers, by running:
-```shell
-make local-start
-```
-It will take a while to run until all the Docker images are pulled or built.
-
-Behind the scenes it will build and run all the Docker images defined in the [docker-compose.yml](https://github.com/decodingml/llm-twin-course/blob/main/docker-compose.yml) file.
-
-> [!CAUTION]
-> For `MongoDB` to work with multiple replicas (as we use it in our Docker setup) on `macOS` or `Linux` systems, you have to add the following lines of code to `/etc/hosts`:
->
-> ```
-> 127.0.0.1       mongo1
-> 127.0.0.1       mongo2 
-> 127.0.0.1       mongo3
-> ```
->
-> From what we know, on `Windows`, it `works out-of-the-box`. For more details, check out this [article](https://medium.com/workleap/the-only-local-mongodb-replica-set-with-docker-compose-guide-youll-ever-need-2f0b74dd8384)
-
-> [!WARNING]
-> For `arm` users (e.g., `M macOS devices`), go to your Docker desktop application and enable `Use Rosetta for x86_64/amd64 emulation on Apple Silicon` from the Settings. There is a checkbox you have to check.
-> Otherwise, your Docker containers will crash.
-
-### Tear down the infrastructure
-
-Run the following `Make` command to tear down all your docker containers:
-
-```shell
-make local-stop
-```
-
-# Usage: Run an end-to-end flow
-
-Now that we have configured our credentials, local environment and Docker infrastructure let's look at how to run an end-to-end flow of the LLM Twin course.
-
-> [!IMPORTANT]
-> Note that we won't go into the details of the system here. To fully understand it, check out our free lessons, which explains everything step-by-step: [LLM Twin articles series](https://medium.com/decodingml/llm-twin-course/home).
-
-### Step 1: Crawling data
-
-Trigger the crawler to collect data and add it to the MongoDB:
-
-```shell
-make local-test-medium
-# or make local-test-github
-``` 
-You should get a response with a `200` status code, as follows:
-```
-{"statusCode": 200, "body": "Link processed successfully"}
-```
-
-After running the command, this will happen:
-1. it will crawl a Medium/GitHub link
-2. process and add the data to MongoDB
-3. the CDC component will be triggered, which will populate the RabbitMQ with the event
-4. the RAG feature pipeline will read the event from RabbitMQ, process it for RAG, and add it to the Qdrant vector DB
-
-You can check the logs from the crawler Docker image, by running:
-```bash
-docker logs llm-twin-data-crawlers
-``` 
-You should see something similar to:
-```text
-{"level":"INFO","location":"extract:53","message":"Finished scrapping custom article: https://medium.com/decodingml/an-end-to-end-framework-for-production-ready-llm-systems-by-building-your-llm-twin-2cc6bb01141f","timestamp":"2024-12-25 17:13:33,630+0000","service":"llm-twin-course/crawler"}
-```
-
-### Step 2: Feature engineering & Vector DB
-
-The previous step actually called both the crawling and RAG feature engineering pipeline. But now, let's check that everything worked as expected.
-
-Thus, let's check that the feature pipeline works and the vector DB is successfully populated.
-
-To do so, check the logs of the `llm-twin-feature-pipeline` Docker container by running:
-```shell
-docker logs llm-twin-feature-pipeline
-```
-You should see something similar to:
-```text
-2024-12-25 16:53:45 [info     ] Cleaned content chunked successfully. cls=data_logic.dispatchers data_type=repositories num=955
-2024-12-25 16:53:45 [info     ] Chunk embedded successfully.   cls=data_logic.dispatchers data_type=repositories embedding_len=384
-2024-12-25 16:53:45 [info     ] Chunk embedded successfully.   cls=data_logic.dispatchers data_type=repositories embedding_len=384
-```
-Also, you can check the logs of the CDC and RabbitMQ Docker containers, by running:
-```bash
-docker logs llm-twin-data-cdc # CDC service
-docker logs llm-twin-mq # RabbitMQ
-```
-
-You should see logs reflecting the cleaning, chunking, and embedding operations (without any errors, of course).
-
-To check that the Qdrant `vector DB` is populated successfully, go to its dashboard by typing in your browser: **[localhost:6333/dashboard](localhost:6333/dashboard)**. There, you should see the repositories or article collections created and populated, similar to the image below:
-
-![Qdrant Example](media/qdrant-example.png)
-
-> [!NOTE]
-> If using the cloud version of Qdrant, go to your Qdrant account and cluster to see the same thing as in the local dashboard.
-
-### Step 3: Populating MongoDB and Qdrant with more data
-
-To populate MongoDB and VectorDB with ~50 links, run the following command (but first make sure that everything runs smooth):
-```bash
-make local-ingest-data
-```
-**Note:** To crawl different links, you can change the `data/links.txt` file. 
-
-### Step 4: Testing the RAG retrieval step
-
-Now that our Qdrant vector DB is populated with our data let's test out the RAG retrieval module to see that everything works fine by retrieving some items using a dummy query:
-```shell
-make local-test-retriever
-```
-
-> [!IMPORTANT]
-> Before running this command, check [Qdrant's dashboard](localhost:6333/dashboard) to ensure your vector DB is populated with data. Otherwise, the retriever will not have any data to work with.
-
-### Step 5: Generating the instruct dataset
-
-The last step before fine-tuning is to generate an instruct dataset and track it as an artifact with Comet ML. To do so, run:
-```shell
-make local-generate-instruct-dataset
-```
-
-Now go to [Comet ML](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github), then to your workspace, and open the `Artifacts` tab. There, you should find three artifacts as follows:
-> - `articles-instruct-dataset` 
-> - `posts-instruct-dataset`
-> - `repositories-instruct-dataset`
-
-To download the instruct dataset from Comet, run:
-```bash
-make download-instruct-dataset
-```
-
-> [!NOTE]
-> We also publicly provide our own artifacts that you can use to fine-tune your LLMs in case you got stuck or don't want to generate them yourself:
-> - [articles-instruct-dataset](https://www.comet.com/decodingml/artifacts/articles-instruct-dataset)
-> - [posts-instruct-dataset](https://www.comet.com/decodingml/artifacts/posts-instruct-dataset)
-> - [repositories-instruct-dataset](https://www.comet.com/decodingml/artifacts/repositories-instruct-dataset)
-
-### Step 6: Setting up AWS SageMaker
-
-For your AWS set-up to work correctly, you need the AWS CLI installed on your local machine and properly configured with an admin user (or a user with enough permissions to create new SageMaker, ECR and S3 resources; using an admin user will make everything more straightforward).
-
-With the same configuration used to set up your AWS CLI, also fill in the following environment variables from your `.env` file:
-```bash
-AWS_REGION=eu-central-1
-AWS_ACCESS_KEY=str
-AWS_SECRET_KEY=str
-```
-AWS credentials are typically stored in `~/.aws/credentials`. You can view this file directly using `cat` or similar commands:
-```shell
-cat ~/.aws/credentials
-```
-
-The next step is to create an IAM execution role used by AWS SageMaker to access other AWS resources. This is standard practice when working with SageMaker.
-
-To automatically create it, run:
-```bash
-make create-sagemaker-execution-role
-```
-
-The script will generate a file found at `src/sagemaker_execution_role.json`. Open it, copy the value under the `RoleArn` key from the JSON file, and fill in the following env var from your `.env` file:
-```bash
-AWS_ARN_ROLE=str
-```
-
-To conclude, by the end of this section you should have filled correctly the following environment variables in your `.env` file:
-```bash
-AWS_ARN_ROLE=str
-AWS_REGION=eu-central-1
-AWS_ACCESS_KEY=str
-AWS_SECRET_KEY=str
-```
-
-Now, we can move on to the fine-tunine and inference pipelines, which use AWS SagaMaker.
-
-> [!IMPORTANT]
-> Note that we use `ml.g5.2xlarge` EC2 instances to run AWS SageMaker (which are already set in our settings), which cost `~$2 / hour` (depending on your region). Our tests will take only ~2-3 hours at maximum. Thus, this won't get expensive. Just run our clean-up resources scripts after you finish testing our app: `make delete-inference-pipeline-deployment`
-
-### Step 7: Starting the fine-tuning pipeline
-
-First, go to our base model, which we will use for fine-tuning on Hugging Face -> [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) and get access to use it. Everything is free.
-
-Next, after setting up everything necessary for AWS SageMaker and Llama-3.1-8B, kicking off the training in dummy mode is as easy as running the following (dummy mode will reduce the dataset size and epochs to quickly see that everything works fine, reducing the running time <30 minutes):
-```bash
-make start-training-pipeline-dummy-mode
-```
-
-To kick off the full training, run (which will take ~2-3 hours, using our dataset and preconfigured settings):
-```bash
-make start-training-pipeline
-```
-
-Go to your [Hugging Face account](https://huggingface.co/), and under the **Models** section, you will see your `{your_name}/LLMTwin-Llama-3.1-8B` model. To run the evaluation and inference using your fine-tuned LLM, add your Hugging Face model ID to your `.env` file, such as `MODEL_ID=pauliusztin/LLMTwin-Llama-3.1-8B`.
-
-> [!NOTE]
-> You can check out the deployment progress in the AWS console in the SageMaker dashboard.
-
-> [!WARNING]
-> If you get any `Service Quotas` errors, you must increase your AWS quotas for `ml.g5.2xlarge` instances (which is free). More exactly, you have to go to your AWS account -> Service Quatas -> AWS services -> search `SageMaker` -> search `ml.g5.2xlarge`, then increase the quotas to 1 for `ml.g5.2xlarge for training job usage` (training jobs) and `ml.g5.2xlarge for endpoint usage` (inference jobs). More details on changing service quotas are in [this article](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html).
-
-
-### Step 8: Runing the evaluation pipelines
-
-After you have finetuned your LLM, you can start the LLM evaluation pipeline by running:
-```shell
-make evaluate-llm
-```
-
-To start the RAG evaluation pipeline, run:
-```shell
-make evaluate-rag
-```
-
-Next, check the evaluation datasets and experiment results in [Opik's Dashboard](https://www.comet.com/opik).
-
-
-### Step 9: Testing the inference pipeline
-
-After you have finetuned and evaluated your model, the first step is to deploy the LLM to AWS SageMaker as a REST API service:
-```shell
-make deploy-inference-pipeline 
-```
-
-> [!NOTE]
-> You can check out the deployment progress in the AWS console in the SageMaker dashboard.
-
-After the deployment is finished (it will take a few minutes), you can call it with a test prompt by running:
-```shell
-make call-inference-pipeline
-```
-
-After testing the inference pipeline from the CLI, you can start playing around with the LLM Twin from our GUI, by running:
-```shell
-make local-start-ui
-```
-
-Now you can access the GUI at **[http://localhost:7860](http://localhost:7860)** and start asking the LLM Twin to generate some content for you, such as: **"Draft a post about RAG systems."** as in the example below:
-
-![UI Example](media/ui-example.png)
-
-After playing around with the model, you will start collecting and monitoring your prompts which you can visualize again in [Opik's Dashboard](https://www.comet.com/opik).
-
-Also, you can kick off the monitoring LLM evaluation pipeline by running:
-```bash
-make evaluate-llm-monitoring
-```
-
-Ultimately, after testing the inference pipeline, you can delete the AWS SageMaker deployment, by running:
-```shell
-make delete-inference-pipeline-deployment
-```
-
-> [!WARNING]
-> Clear your AWS resources to avoid any unexpected costs. Running `make delete-inference-pipeline-deployment` should clear everything, but we recommend double-checking your AWS SageMaker dashboard manually to ensure everything is shut down in case of edge cases.
diff --git a/LICENSE b/LICENSE
index 21618a4..9266bb0 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 Decoding ML by Crafted Intelligence S.R.L.
+Copyright (c) 2024 Decoding ML
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/Makefile b/Makefile
deleted file mode 100644
index fdcdeea..0000000
--- a/Makefile
+++ /dev/null
@@ -1,116 +0,0 @@
-include .env
-
-$(eval export $(shell sed -ne 's/ *#.*$$//; /./ s/=.*$$// p' .env))
-
-PYTHONPATH := $(shell pwd)/src
-
-install: # Create a local Poetry virtual environment and install all required Python dependencies.
-	poetry env use 3.11
-	poetry install --without superlinked_rag
-
-help:
-	@grep -E '^[a-zA-Z0-9 -]+:.*#'  Makefile | sort | while read -r l; do printf "\033[1;32m$$(echo $$l | cut -f 1 -d':')\033[00m:$$(echo $$l | cut -f 2- -d'#')\n"; done
-
-# ======================================
-# ------- Docker Infrastructure --------
-# ======================================
-
-local-start: # Build and start your local Docker infrastructure.
-	docker compose -f docker-compose.yml up --build -d
-
-local-stop: # Stop your local Docker infrastructure.
-	docker compose -f docker-compose.yml down --remove-orphans
-
-# ======================================
-# ---------- Crawling Data -------------
-# ======================================
-
-local-test-medium: # Make a call to your local AWS Lambda (hosted in Docker) to crawl a Medium article.
-	curl -X POST "http://localhost:9010/2015-03-31/functions/function/invocations" \
-	  	-d '{"user": "Paul Iusztin", "link": "https://medium.com/decodingml/an-end-to-end-framework-for-production-ready-llm-systems-by-building-your-llm-twin-2cc6bb01141f"}'
-
-local-test-github: # Make a call to your local AWS Lambda (hosted in Docker) to crawl a Github repository.
-	curl -X POST "http://localhost:9010/2015-03-31/functions/function/invocations" \
-	  	-d '{"user": "Paul Iusztin", "link": "https://github.com/decodingml/llm-twin-course"}'
-
-local-ingest-data: # Ingest all links from data/links.txt by calling your local AWS Lambda hosted in Docker.
-	while IFS= read -r link; do \
-		echo "Processing: $$link"; \
-		curl -X POST "http://localhost:9010/2015-03-31/functions/function/invocations" \
-			-d "{\"user\": \"Paul Iusztin\", \"link\": \"$$link\"}"; \
-		echo "\n"; \
-		sleep 2; \
-	done < data/links.txt
-
-# ======================================
-# -------- RAG Feature Pipeline --------
-# ======================================
-
-local-test-retriever: # Test the RAG retriever using your Poetry env
-	cd src/feature_pipeline && poetry run python -m retriever
-
-local-generate-instruct-dataset: # Generate the fine-tuning instruct dataset using your Poetry env.
-	cd src/feature_pipeline && poetry run python -m generate_dataset.generate
-
-# ===================================================
-# -- AWS SageMaker: Training & Inference pipelines --
-# ===================================================
-
-download-instruct-dataset: # Download the fine-tuning instruct dataset using your Poetry env.
-	cd src/training_pipeline && PYTHONPATH=$(PYTHONPATH) poetry run python download_dataset.py
-
-create-sagemaker-execution-role: # Create an AWS SageMaker execution role you need for the training and inference pipelines.
-	cd src && PYTHONPATH=$(PYTHONPATH) poetry run python -m core.aws.create_execution_role
-
-start-training-pipeline-dummy-mode: # Start the training pipeline in AWS SageMaker.
-	cd src/training_pipeline && poetry run python run_on_sagemaker.py --is-dummy
-
-start-training-pipeline: # Start the training pipeline in AWS SageMaker.
-	cd src/training_pipeline && poetry run python run_on_sagemaker.py
-
-local-start-training-pipeline: # Start the training pipeline in your Poetry env.
-	cd src/training_pipeline && poetry run python -m finetune
-
-deploy-inference-pipeline: # Deploy the inference pipeline to AWS SageMaker.
-	cd src/inference_pipeline && poetry run python -m aws.deploy_sagemaker_endpoint
-
-call-inference-pipeline: # Call the inference pipeline client using your Poetry env.
-	cd src/inference_pipeline && poetry run python -m main
-
-delete-inference-pipeline-deployment: # Delete the deployment of the AWS SageMaker inference pipeline.
-	cd src/inference_pipeline && PYTHONPATH=$(PYTHONPATH) poetry run python -m aws.delete_sagemaker_endpoint
-
-local-start-ui: # Start the Gradio UI for chatting with your LLM Twin using your Poetry env.
-	cd src/inference_pipeline && poetry run python -m ui
-
-evaluate-llm: # Run evaluation tests on the LLM model's performance using your Poetry env.
-	cd src/inference_pipeline && poetry run python -m evaluation.evaluate
-
-evaluate-rag: # Run evaluation tests specifically on the RAG system's performance using your Poetry env.
-	cd src/inference_pipeline && poetry run python -m evaluation.evaluate_rag
-
-evaluate-llm-monitoring: # Run evaluation tests for monitoring the LLM system using your Poetry env.
-	cd src/inference_pipeline && poetry run python -m evaluation.evaluate_monitoring
-
-# ======================================
-# ------ Superlinked Bonus Series ------
-# ======================================
-
-install-superlinked: # Create a local Poetry virtual environment and install all required Python dependencies (with Superlinked enabled).
-	poetry env use 3.11
-	poetry install
-
-local-start-superlinked: # Build and start local infrastructure used in the Superlinked series.
-	docker compose -f docker-compose-superlinked.yml up --build -d
-
-local-stop-superlinked: # Stop local infrastructure used in the Superlinked series.
-	docker compose -f docker-compose-superlinked.yml down --remove-orphans
-
-test-superlinked-server: # Ingest dummy data into the local superlinked server to check if it's working.
-	poetry run python src/bonus_superlinked_rag/local_test.py
-
-local-bytewax-superlinked: # Run the Bytewax streaming pipeline powered by Superlinked.
-	RUST_BACKTRACE=full poetry run python -m bytewax.run src/bonus_superlinked_rag/main.py
-
-local-test-retriever-superlinked: # Call the retrieval module and query the Superlinked server & vector DB
-	docker exec -it llm-twin-bytewax-superlinked python -m retriever
diff --git a/README.md b/README.md
index 4b44c8e..9ede619 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 <div align="center">
     <h2>LLM Twin Course: Building Your Production-Ready AI Replica</h2>
-    <h1>Learn to architect and implement a production-ready LLM & RAG system by building your LLM Twin</h1>
+    <h1>An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin</h1>
     <h3>From data gathering to productionizing LLMs using LLMOps good practices.</h3>
-    <i>by <a href="https://decodingml.substack.com">Decoding ML</i>
+    <i>by <a href="https://github.com/iusztinpaul">Paul Iusztin</a>, <a href="https://github.com/alexandruvesa">Alexandru Vesa</a> and <a href="https://github.com/Joywalker">Alexandru Razvant</a></i>
 </div>
 
 </br>
@@ -11,13 +11,18 @@
   <img src="media/cover.png" alt="Your image description">
 </p>
 
-## 🎯 What you'll learn
+</br>
+
+## Why is this course different?
 
 *By finishing the **"LLM Twin: Building Your Production-Ready AI Replica"** free course, you will learn how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.*
 
-**No more isolated scripts or Notebooks!** Learn production ML by building and deploying an end-to-end production-grade LLM system.
+> Why should you care? 🫵
+> 
+> → **No more isolated scripts or Notebooks!** Learn production ML by building and deploying an end-to-end production-grade LLM system.
+
 
-## 📖 About this course
+## What will you learn to build by the end of this course?
 
 You will **learn** how to **architect** and **build a real-world LLM system** from **start** to **finish** - from **data collection** to **deployment**.
 
@@ -27,316 +32,131 @@ You will also **learn** to **leverage MLOps best practices**, such as experiment
 
 **What is an LLM Twin?** It is an AI character that learns to write like somebody by incorporating its style and personality into an LLM.
 
-## 🪈 The architecture of the LLM Twin is split into 4 Python microservices
-
-<p align="center">
-  <img src="media/architecture.png" alt="LLM Twin Architecture">
-</p>
+## The architecture of the LLM twin is split into 4 Python microservices:
 
 ### The data collection pipeline
 
-- Crawl your digital data from various social media platforms, such as Medium, Substack and GitHub.
+- Crawl your digital data from various social media platforms.
 - Clean, normalize and load the data to a [Mongo NoSQL DB](https://www.mongodb.com/) through a series of ETL pipelines.
 - Send database changes to a [RabbitMQ](https://www.rabbitmq.com/) queue using the CDC pattern.
-- Learn to package the crawlers as AWS Lambda functions.
+- ☁️ Deployed on [AWS](https://aws.amazon.com/).
 
 ### The feature pipeline
 
-- Consume messages in real-time from a queue through a [Bytewax](https://github.com/bytewax/bytewax?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1) streaming pipeline.
-- Every message will be cleaned, chunked, embedded and loaded into a [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) vector DB.
-- In the bonus series, we refactor the cleaning, chunking, and embedding logic using [Superlinked](https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse), a specialized vector compute engine. We will also load and index the vectors to a [Redis vector DB](https://redis.io/solutions/vector-search/).
+- Consume messages from a queue through a [Bytewax](https://github.com/bytewax/bytewax?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1) streaming pipeline.
+- Every message will be cleaned, chunked, embedded (using [Superlinked](https://github.com/superlinked/superlinked-alpha?utm_source=community&utm_medium=github&utm_campaign=oscourse), and loaded into a [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) vector DB in real-time.
+- ☁️ Deployed on [AWS](https://aws.amazon.com/).
 
 ### The training pipeline
-
-- Create a custom instruction dataset based on your custom digital data to do SFT.
-- Fine-tune an LLM using LoRA or QLoRA.
+- Create a custom dataset based on your digital data.
+- Fine-tune an LLM using QLoRA.
 - Use [Comet ML's](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) experiment tracker to monitor the experiments.
-- Evaluate the LLM using [Opik](https://github.com/comet-ml/opik)
-- Save and version the best model to the [Hugging Face model registry](https://huggingface.co/models).
-- Run and automate the training pipeline using [AWS SageMaker](https://aws.amazon.com/sagemaker/).
+- Evaluate and save the best model to [Comet's](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) model registry.
+- ☁️ Deployed on [Qwak](https://www.qwak.com/lp/end-to-end-mlops/?utm_source=github&utm_medium=referral&utm_campaign=decodingml).
 
 ### The inference pipeline
+- Load and quantize the fine-tuned LLM from [Comet's](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) model registry.
+- Deploy it as a REST API.
+- Enhance the prompts using RAG.
+- Generate content using your LLM twin.
+- Monitor the LLM using [Comet's](https://www.comet.com/signup/?framework=llm&utm_source=decoding_ml&utm_medium=partner&utm_content=github) prompt monitoring dashboard.
+- ☁️ Deployed on [Qwak](https://www.qwak.com/lp/end-to-end-mlops/?utm_source=github&utm_medium=referral&utm_campaign=decodingml).
 
-- Load the fine-tuned LLM from the [Hugging Face model registry](https://huggingface.co/models).
-- Deploy the LLM as a scalable REST API using [AWS SageMaker inference endpoints](https://aws.amazon.com/sagemaker/deploy/).
-- Enhance the prompts using advanced RAG techniques.
-- Monitor the prompts and LLM generated results using [Opik](https://github.com/comet-ml/opik)
-- In the bonus series, we refactor the advanced RAG layer to write more optimal queries using [Superlinked](https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse).
-- Wrap up everything with a Gradio UI (as seen below) where you can start playing around with the LLM Twin to generate content that follows your writing style.
-
-<p align="center">
-  <img src="media/ui-example.png" alt="Gradio UI">
-</p>
-
-Along the 4 microservices, you will learn to integrate 4 serverless tools:
-
-* [Comet ML](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) as your experiment tracker and data registry;
-* [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) as your vector DB;
-* [AWS SageMaker](https://aws.amazon.com/sagemaker/) as your ML infrastructure;
-* [Opik](https://github.com/comet-ml/opik) as your prompt evaluation and monitoring tool.
-
-----
-
-<table style="border-collapse: collapse; border: none;">
-  <tr style="border: none;">
-    <td width="20%" style="border: none;">
-      <a href="https://decodingml.substack.com/" aria-label="Decoding ML">
-        <img src="https://github.com/user-attachments/assets/f2f2f9c0-54b7-4ae3-bf8d-23a359c86982" alt="Decoding ML Logo" width="150"/>
-      </a>
-    </td>
-    <td width="80%" style="border: none;">
-      <div>
-        <h2>📬 Stay Updated</h2>
-        <p><b><a href="https://decodingml.substack.com/">Join Decoding ML</a></b> for proven content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications. Every week, straight to your inbox.</p>
-      </div>
-    </td>
-  </tr>
-</table>
+</br>
 
 <p align="center">
-  <a href="https://decodingml.substack.com/">
-    <img src="https://img.shields.io/static/v1?label&logo=substack&message=Subscribe%20Now&style=for-the-badge&color=black&scale=2" alt="Subscribe Now" height="40">
-  </a>
+  <img src="media/architecture.png" alt="Your image description">
 </p>
 
-## 👥 Who should join?
+</br>
 
-**This course is ideal for:**
-- ML/AI engineers who want to learn to engineer production-ready LLM & RAG systems using LLMOps good principles
-- Data Engineers, Data Scientists, and Software Engineers wanting to understand the engineering behind LLM & RAG systems
+Along the 4 microservices, you will learn to integrate 3 serverless tools:
 
-**Note:** This course focuses on engineering practices and end-to-end system implementation rather than theoretical model optimization or research.
+* [Comet ML](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) as your ML Platform;
+* [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) as your vector DB;
+* [Qwak](https://www.qwak.com/lp/end-to-end-mlops/?utm_source=github&utm_medium=referral&utm_campaign=decodingml) as your ML infrastructure;
 
-## 🎓 Prerequisites
+## Who is this for?
 
-| Category | Requirements |
-|----------|-------------|
-| **Skills** | Basic understanding of Python and Machine Learning |
-| **Hardware** | Any modern laptop/workstation will do the job, as the LLM fine-tuning and inference will be done on AWS SageMaker. |
-| **Level** | Intermediate |
+**Audience:** MLE, DE, DS, or SWE who want to learn to engineer production-ready LLM systems using LLMOps good principles.
 
-## 💰 Cost structure
+**Level:** intermediate
 
-All tools used throughout the course will stick to their free tier, except:
+**Prerequisites:** basic knowledge of Python, ML, and the cloud
 
-- OpenAI's API, which will cost ~$1
-- AWS for fine-tuning and inference, which will cost < $10 depending on how much you play around with our scripts and your region.
+## How will you learn?
 
-## 🥂 Open-source course: Participation is open and Free
+The course contains **11 hands-on written lessons** and the **open-source code** you can access on GitHub.
 
-As an open-source course, you don't have to enroll. Everything is self-paced, free of charge and with its resources freely accessible as follows:
-- **code**: this GitHub repository
-- **articles**: [Decoding ML](https://medium.com/decodingml/llm-twin-course/home)
+You can read everything and try out the code at your own pace. 
 
-## 📚 How will you learn?
+## Costs?
+The **articles** and **code** are **completely free**. They will always remain free.
 
-The course contains **10 hands-on written lessons** and the **open-source code** you can access on GitHub, showing how to build an end-to-end LLM system.
+But if you plan to run the code while reading it, you have to know that we use several cloud tools that might generate additional costs.
 
-Also, it includes **2 bonus lessons** on how to **improve the RAG system**.
+The cloud computing platforms ([AWS](https://aws.amazon.com/), [Qwak](https://www.qwak.com/lp/end-to-end-mlops/?utm_source=github&utm_medium=referral&utm_campaign=decodingml)) have a pay-as-you-go pricing plan. Qwak offers a few hours of free computing. Thus, we did our best to keep costs to a minimum.
 
-You can read everything at your own pace.
+For the other serverless tools ([Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course), [Comet](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github)), we will stick to their freemium version, which is free of charge.
 
 ## Lessons
 
-This self-paced course consists of 12 comprehensive lessons covering theory, system design, and hands-on implementation.
-
-Our recommendation for each module:
-1. Read the article
-2. Run the code to replicate our results
-3. Go deeper into the code by reading the `src` Python modules
-
-> [!NOTE]
-> Check the [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) doc for a step-by-step installation and usage guide.
-
-| Lesson | Article | Category | Description | Source Code |
-|--------|---------|----------|-------------|-------------|
-| 1 | [An End-to-End Framework for Production-Ready LLM Systems](https://medium.com/decodingml/an-end-to-end-framework-for-production-ready-llm-systems-by-building-your-llm-twin-2cc6bb01141f) | System Design | Learn the overall architecture and design principles of production LLM systems. | No code |
-| 2 | [Data Crawling](https://medium.com/decodingml/your-content-is-gold-i-turned-3-years-of-blog-posts-into-an-llm-training-d19c265bdd6e) | Data Engineering | Learn to crawl and process social media content for LLM training. | `src/data_crawling` |
-| 3 | [CDC Magic](https://medium.com/decodingml/i-replaced-1000-lines-of-polling-code-with-50-lines-of-cdc-magic-4d31abd3bc3b) | Data Engineering | Learn to implement Change Data Capture (CDC) for syncing two data sources. | `src/data_cdc` |
-| 4 | [Feature Streaming Pipelines](https://medium.com/decodingml/sota-python-streaming-pipelines-for-fine-tuning-llms-and-rag-in-real-time-82eb07795b87) | Feature Pipeline | Build real-time streaming pipelines for LLM and RAG data processing. | `src/feature_pipeline` |
-| 5 | [Advanced RAG Algorithms](https://medium.com/decodingml/the-4-advanced-rag-algorithms-you-must-know-to-implement-5d0c7f1199d2) | Feature Pipeline | Implement advanced RAG techniques for better retrieval. | `src/feature_pipeline` |
-| 6 | [Generate Fine-Tuning Instruct Datasets](https://medium.com/decodingml/turning-raw-data-into-fine-tuning-datasets-dc83657d1280) | Training Pipeline | Create custom instruct datasets for LLM fine-tuning. | `src/feature_pipeline/generate_dataset` |
-| 7 | [LLM Fine-tuning Pipeline](https://medium.com/decodingml/8b-parameters-1-gpu-no-problems-the-ultimate-llm-fine-tuning-pipeline-f68ef6c359c2) | Training Pipeline | Build an end-to-end LLM fine-tuning pipeline and deploy it to AWS SageMaker. | `src/training_pipeline` |
-| 8 | [LLM & RAG Evaluation](https://medium.com/decodingml/the-engineers-framework-for-llm-rag-evaluation-59897381c326) | Training Pipeline | Learn to evaluate LLM and RAG system performance. | `src/inference_pipeline/evaluation` |
-| 9 | [Implement and Deploy the RAG Inference Pipeline](https://medium.com/decodingml/beyond-proof-of-concept-building-rag-systems-that-scale-e537d0eb063a) | Inference Pipeline | Design,  implement and deploy the RAG inference to AWS SageMaker. | `src/inference_pipeline` |
-| 10 | [Prompt Monitoring](https://medium.com/decodingml/the-ultimate-prompt-monitoring-pipeline-886cbb75ae25) | Inference Pipeline | Build the prompt monitoring and production evaluation pipeline. | `src/inference_pipeline` |
-| 11 | [Refactor the RAG module using 74.3% Less Code ](https://medium.com/decodingml/build-a-scalable-rag-ingestion-pipeline-using-74-3-less-code-ac50095100d6) | Bonus on RAG | Optimize the RAG system. | `src/bonus_superlinked_rag` |
-| 12 | [Multi-Index RAG Apps](https://medium.com/decodingml/build-multi-index-advanced-rag-apps-bd33d2f0ec5c) | Bonus on RAG | Build advanced multi-index RAG apps. | `src/bonus_superlinked_rag` |
-
-----
-
-<table style="border-collapse: collapse; border: none;">
-  <tr style="border: none;">
-    <td width="20%" style="border: none;">
-      <a href="https://decodingml.substack.com/" aria-label="Decoding ML">
-        <img src="https://github.com/user-attachments/assets/f2f2f9c0-54b7-4ae3-bf8d-23a359c86982" alt="Decoding ML Logo" width="150"/>
-      </a>
-    </td>
-    <td width="80%" style="border: none;">
-      <div>
-        <h2>📬 Stay Updated</h2>
-        <p><b><a href="https://decodingml.substack.com/">Join Decoding ML</a></b> for proven content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications. Every week, straight to your inbox.</p>
-      </div>
-    </td>
-  </tr>
-</table>
-
-<p align="center">
-  <a href="https://decodingml.substack.com/">
-    <img src="https://img.shields.io/static/v1?label&logo=substack&message=Subscribe%20Now&style=for-the-badge&color=black&scale=2" alt="Subscribe Now" height="40">
-  </a>
-</p>
-
-## 🏗️ Project Structure
-
-At Decoding ML we teach how to build production ML systems, thus the course follows the structure of a real-world Python project:
-
-```text
-llm-twin-course/
-├── src/                     # Source code for all the ML pipelines and services
-│ ├── data_crawling/         # Data collection pipeline code
-│ ├── data_cdc/              # Change Data Capture (CDC) pipeline code
-│ ├── feature_pipeline/      # Feature engineering pipeline code
-│ ├── training_pipeline/     # Training pipeline code
-│ ├── inference_pipeline/    # Inference service code
-│ └── bonus_superlinked_rag/ # Bonus RAG optimization code
-├── .env.example             # Example environment variables template
-├── Makefile                 # Commands to build and run the project
-├── pyproject.toml           # Project dependencies
-```
-
-
-## 🚀 Install & Usage
-
-To understand how to **install and run the LLM Twin code end-to-end**, go to the [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) dedicated document.
-
-> [!NOTE]
-> Even though you can run everything solely using the [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) dedicated document, we recommend that you read the articles to understand the LLM Twin system and design choices fully.
-
-
-## 💡 Questions and troubleshooting
-
-Have questions or running into issues? We're here to help!
-
-Open a [GitHub issue](https://github.com/decodingml/llm-twin-course/issues) for:
-- Questions about the course material
-- Technical troubleshooting
-- Clarification on concepts
+> [!IMPORTANT]
+> **The course is a work in progress. We plan to release a new lesson every 2 weeks.**
 
+`To understand the entire code step-by-step, check out our articles` ↓
 
-## 🥂 Contributing
+> The course is split into 11 lessons. Every Medium article will be its own lesson.
 
-As an open-source course, we may not be able to fix all the bugs that arise.
+1. [An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin](https://medium.com/decodingml/an-end-to-end-framework-for-production-ready-llm-systems-by-building-your-llm-twin-2cc6bb01141f)
+2. [The Importance of Data Pipelines in the Era of Generative AI](https://medium.com/decodingml/the-importance-of-data-pipelines-in-the-era-of-generative-ai-673e1505a861)
+3. [Change Data Capture: Enabling Event-Driven Architectures](https://medium.com/decodingml/the-3nd-out-of-11-lessons-of-the-llm-twin-free-course-ba82752dad5a)
+4. [SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG — in Real-Time!](https://medium.com/decodingml/sota-python-streaming-pipelines-for-fine-tuning-llms-and-rag-in-real-time-82eb07795b87)
+5. Vector DB retrieval clients [Module 2] …WIP
+6. Training data preparation [Module 3] …WIP
+7. Fine-tuning LLM [Module 3] …WIP
+8. LLM evaluation [Module 4] …WIP
+9. Quantization [Module 5] …WIP 
+10. Build the digital twin inference pipeline [Module 6] …WIP
+11. Deploy the digital twin as a REST API [Module 6] …WIP
 
-If you find any bugs and know how to fix them, support future readers by contributing to this course with your bug fix.
-
-We will deeply appreciate your support for the AI community and future readers 🤗
-
-
-## Contributors
-
-A big "Thank you 🙏" to all our contributors! This course is possible only because of their efforts.
-
-<p align="center">
-    <a href="https://github.com/decodingml/llm-twin-course/graphs/contributors">
-      <img src="https://contrib.rocks/image?repo=decodingml/llm-twin-course" />
-    </a>
-</p>
-
-## Sponsors
-
-Also, another big "Thank you 🙏" to all our sponsors who supported our work and made this course possible. 
+## Meet your teachers!
+The course is created under the [Decoding ML](https://decodingml.substack.com/) umbrella by:
 
 <table>
   <tr>
-    <td align="center">
-      <a href="https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github" target="_blank">Comet</a>
-    </td>
-    <td align="center">
-      <a href="https://github.com/comet-ml/opik" target="_blank">Opik</a>
-    </td>
-    <td align="center">
-      <a href="https://github.com/bytewax/bytewax?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1" target="_blank">Bytewax</a>
-    </td>
-    <td align="center">
-      <a href="https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course" target="_blank">Qdrant</a>
-    </td>
-    <td align="center">
-      <a href="https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse" target="_blank">Superlinked</a>
+    <td><a href="https://github.com/iusztinpaul" target="_blank"><img src="https://github.com/iusztinpaul.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Paul Iusztin</strong><br />
+      <i>Senior ML & MLOps Engineer</i>
     </td>
   </tr>
   <tr>
-    <td align="center">
-      <a href="https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github" target="_blank">
-        <img src="media/sponsors/comet.png" width="150" alt="Comet">
-      </a>
-    </td>
-    <td align="center">
-      <a href="https://github.com/comet-ml/opik" target="_blank">
-        <img src="media/sponsors/opik.svg" width="150" alt="Opik">
-      </a>
-    </td>
-    <td align="center">
-      <a href="https://github.com/bytewax/bytewax?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1" target="_blank">
-        <img src="media/sponsors/bytewax.png" width="150" alt="Bytewax">
-      </a>
-    </td>
-    <td align="center">
-      <a href="https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course" target="_blank">
-        <img src="media/sponsors/qdrant.svg" width="150" alt="Qdrant">
-      </a>
+    <td><a href="https://github.com/alexandruvesa" target="_blank"><img src="https://github.com/alexandruvesa.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Alexandru Vesa</strong><br />
+      <i>Senior AI Engineer</i>
     </td>
-    <td align="center">
-      <a href="https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse" target="_blank">
-        <img src="media/sponsors/superlinked.svg" width="150" alt="Superlinked">
-      </a>
+  </tr>
+  <tr>
+    <td><a href="https://github.com/Joywalker" target="_blank"><img src="https://github.com/Joywalker.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Răzvanț Alexandru</strong><br />
+      <i>Senior ML Engineer</i>
     </td>
   </tr>
 </table>
 
-## Next steps
-
-Our **LLM Engineer’s Handbook** inspired the **open-source LLM Twin course**.
-
-Consider supporting our work by getting our book to **learn** a **complete framework** for **building and deploying production LLM & RAG systems** — from data to deployment.
-
-Perfect for practitioners who want **both theory** and **hands-on** expertise by connecting the dots between DE, research, MLE and MLOps:
-
-**[Buy the LLM Engineer’s Handbook](https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/)**
-
-* [On Amazon](https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/)
-* [On Packt](https://www.packtpub.com/en-us/product/llm-engineers-handbook-9781836200062)
-
-<p align="center">
-  <a href="https://www.amazon.com/LLM-Engineers-Handbook-engineering-production/dp/1836200072/">
-    <img src="media/llm_engineers_handbook_cover.png" alt="LLM Engineer's Handbook">
-  </a>
-</p>
-
 ## License
 
 This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).
 
-----
+## 🏆 Contribution
 
-<table style="border-collapse: collapse; border: none;">
-  <tr style="border: none;">
-    <td width="20%" style="border: none;">
-      <a href="https://decodingml.substack.com/" aria-label="Decoding ML">
-        <img src="https://github.com/user-attachments/assets/f2f2f9c0-54b7-4ae3-bf8d-23a359c86982" alt="Decoding ML Logo" width="150"/>
-      </a>
-    </td>
-    <td width="80%" style="border: none;">
-      <div>
-        <h2>📬 Stay Updated</h2>
-        <p><b><a href="https://decodingml.substack.com/">Join Decoding ML</a></b> for proven content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications. Every week, straight to your inbox.</p>
-      </div>
-    </td>
-  </tr>
-</table>
+A big "Thank you 🙏" to all our contributors! This course is possible only because of their efforts.
 
 <p align="center">
-  <a href="https://decodingml.substack.com/">
-    <img src="https://img.shields.io/static/v1?label&logo=substack&message=Subscribe%20Now&style=for-the-badge&color=black&scale=2" alt="Subscribe Now" height="40">
-  </a>
+    <a href="https://github.com/decodingml/llm-twin-course/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=decodingml/llm-twin-course" />
+    </a>
 </p>
diff --git a/course/module-1/.env.example b/course/module-1/.env.example
new file mode 100644
index 0000000..9adc213
--- /dev/null
+++ b/course/module-1/.env.example
@@ -0,0 +1,6 @@
+DATABASE_HOST=mongodb://decodingml:decodingml@decodingml_mongo:27017
+DATABASE_NAME=twin
+
+# LinkedIn Credentials
+LINKEDIN_USERNAME=str
+LINKEDIN_PASSWORD=str
diff --git a/course/module-1/.gitignore b/course/module-1/.gitignore
new file mode 100644
index 0000000..b59aba6
--- /dev/null
+++ b/course/module-1/.gitignore
@@ -0,0 +1,163 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# .DS_Store
+.DS_Store
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
\ No newline at end of file
diff --git a/.python-version b/course/module-1/.python-version
similarity index 100%
rename from .python-version
rename to course/module-1/.python-version
diff --git a/.docker/Dockerfile.data_crawlers b/course/module-1/Dockerfile
similarity index 57%
rename from .docker/Dockerfile.data_crawlers
rename to course/module-1/Dockerfile
index b9c8ae3..1625828 100644
--- a/.docker/Dockerfile.data_crawlers
+++ b/course/module-1/Dockerfile
@@ -2,15 +2,13 @@ FROM  public.ecr.aws/lambda/python:3.11 as build
 
 # Install chrome driver and browser
 RUN yum install -y unzip && \
-    curl -Lo "/tmp/chromedriver.zip" "https://storage.googleapis.com/chrome-for-testing-public/126.0.6478.126/linux64/chromedriver-linux64.zip" && \
-    curl -Lo "/tmp/chrome-linux.zip" "https://storage.googleapis.com/chrome-for-testing-public/126.0.6478.126/linux64/chrome-linux64.zip" && \
+    curl -Lo "/tmp/chromedriver.zip" "https://chromedriver.storage.googleapis.com/114.0.5735.90/chromedriver_linux64.zip" && \
+    curl -Lo "/tmp/chrome-linux.zip" "https://www.googleapis.com/download/storage/v1/b/chromium-browser-snapshots/o/Linux_x64%2F1135561%2Fchrome-linux.zip?alt=media" && \
     unzip /tmp/chromedriver.zip -d /opt/ && \
     unzip /tmp/chrome-linux.zip -d /opt/
 
 FROM  public.ecr.aws/lambda/python:3.11
 
-ENV POETRY_VERSION=1.8.3
-
 # Install the function's OS dependencies using yum
 RUN yum install -y \
     atk \
@@ -43,25 +41,23 @@ RUN yum install -y \
     libpq-dev
 
 
-COPY --from=build /opt/chrome-linux64 /opt/chrome
-COPY --from=build /opt/chromedriver-linux64 /opt/
+COPY --from=build /opt/chrome-linux /opt/chrome
+COPY --from=build /opt/chromedriver /opt/
 
-COPY ./pyproject.toml ./poetry.lock ./
+COPY poetry.lock pyproject.toml ./
 
 # Install Poetry, export dependencies to requirements.txt, and install dependencies
 # in the Lambda task directory, finally cleanup manifest files.
-RUN python -m pip install --upgrade pip && pip install --no-cache-dir "poetry==$POETRY_VERSION"
-RUN poetry export --without feature_pipeline -f requirements.txt > requirements.txt && \
-    pip install --no-cache-dir -r requirements.txt --target "${LAMBDA_TASK_ROOT}" && \
+RUN python3 -m pip install --upgrade pip && pip3 install poetry
+RUN poetry export -f requirements.txt > requirements.txt && \
+    pip3 install --no-cache-dir -r requirements.txt --target "${LAMBDA_TASK_ROOT}" && \
     rm requirements.txt pyproject.toml poetry.lock
 
 # Optional TLS CA only if you plan to store the extracted data into Document DB
 RUN wget https://truststore.pki.rds.amazonaws.com/global/global-bundle.pem -P ${LAMBDA_TASK_ROOT}
-ENV PYTHONPATH="${LAMBDA_TASK_ROOT}/data_crawling:${LAMBDA_TASK_ROOT}"
 
 # Copy function code
-COPY ./src/data_crawling ${LAMBDA_TASK_ROOT}/data_crawling
-COPY ./src/core ${LAMBDA_TASK_ROOT}/core
+COPY . ${LAMBDA_TASK_ROOT}
 
 # Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
-CMD ["data_crawling.main.handler"]
+CMD ["main.handler"]
diff --git a/course/module-1/Makefile b/course/module-1/Makefile
new file mode 100644
index 0000000..64e8d4e
--- /dev/null
+++ b/course/module-1/Makefile
@@ -0,0 +1,41 @@
+AWS_CURRENT_REGION_ID := $(shell aws configure get region)
+AWS_CURRENT_ACCOUNT_ID := $(shell aws sts get-caller-identity --query "Account" --output text)
+
+help:
+	@grep -E '^[a-zA-Z0-9 -]+:.*#'  Makefile | sort | while read -r l; do printf "\033[1;32m$$(echo $$l | cut -f 1 -d':')\033[00m:$$(echo $$l | cut -f 2- -d'#')\n"; done
+
+local-build: # Build lambda crawler on local.
+	docker buildx build --platform linux/amd64 -t crawler .
+
+local-deploy: # Deploy lambda crawler custom docker image on local.
+	docker run \
+		-p 9000:8080 \
+		--network llm-twin-course_local \
+		--platform linux/amd64 \
+		crawler:latest
+
+local-test: # Send test command on local to test  the lambda
+	curl -X POST "http://localhost:9000/2015-03-31/functions/function/invocations" \
+	  	-d '{"user": "Paul Iuztin", "link": "https://medium.com/@pauliusztin/the-llms-kit-build-a-production-ready-real-time-financial-advisor-system-using-streaming-ffdcb2b50714"}'
+
+invoke: # Invoke remote lambda from local
+	aws lambda invoke \
+		--function-name crawler \
+		--cli-binary-format raw-in-base64-out \
+		--payload '{"user": "Paul Iuztin", "link": "https://github.com/iusztinpaul/hands-on-llms"}' \
+		response.json
+
+push: # Build & push image to docker ECR (e.g make push IMAGE_TAG=latest)
+	echo "Logging into AWS ECR..."
+	aws ecr get-login-password --region $(AWS_CURRENT_REGION_ID) | docker login --username AWS --password-stdin $(AWS_CURRENT_ACCOUNT_ID).dkr.ecr.$(AWS_CURRENT_REGION_ID).amazonaws.com
+	echo "Build & Push Docker image..."
+	docker buildx build --platform linux/amd64 -t $(AWS_CURRENT_ACCOUNT_ID).dkr.ecr.$(AWS_CURRENT_REGION_ID).amazonaws.com/crawler:$(IMAGE_TAG) .
+	echo "Push completed successfully."
+
+clean: # Cleanup files generated during sam building.
+	@echo "Cleaning old files..."
+	rm -rf /.pytest_cache
+	rm -rf /__pycache
+	rm -rf */.pyc
+	rm -rf .mypy_cache
+	@echo "Done."
diff --git a/course/module-1/Readme.md b/course/module-1/Readme.md
new file mode 100644
index 0000000..a099036
--- /dev/null
+++ b/course/module-1/Readme.md
@@ -0,0 +1 @@
+TBD
diff --git a/course/module-1/config.py b/course/module-1/config.py
new file mode 100644
index 0000000..9680312
--- /dev/null
+++ b/course/module-1/config.py
@@ -0,0 +1,16 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+    # MongoDB
+    DATABASE_HOST: str = "mongodb://decodingml:decodingml@decodingml_mongo:27017"
+    DATABASE_NAME: str = "twin"
+
+    # LinkedIn Credentials
+    LINKEDIN_USERNAME: str | None = None
+    LINKEDIN_PASSWORD: str | None = None
+
+
+settings = Settings()
diff --git a/course/module-1/crawlers/__init__.py b/course/module-1/crawlers/__init__.py
new file mode 100644
index 0000000..91c2799
--- /dev/null
+++ b/course/module-1/crawlers/__init__.py
@@ -0,0 +1,5 @@
+from crawlers.github import GithubCrawler
+from crawlers.linkedin import LinkedInCrawler
+from crawlers.medium import MediumCrawler
+
+__all__ = ["GithubCrawler", "LinkedInCrawler", "MediumCrawler"]
diff --git a/src/data_crawling/crawlers/base.py b/course/module-1/crawlers/base.py
similarity index 82%
rename from src/data_crawling/crawlers/base.py
rename to course/module-1/crawlers/base.py
index d7e5cb4..ae75056 100644
--- a/src/data_crawling/crawlers/base.py
+++ b/course/module-1/crawlers/base.py
@@ -2,7 +2,7 @@
 from abc import ABC, abstractmethod
 from tempfile import mkdtemp
 
-from core.db.documents import BaseDocument
+from documents import BaseDocument
 from selenium import webdriver
 from selenium.webdriver.chrome.options import Options
 
@@ -17,25 +17,28 @@ def extract(self, link: str, **kwargs) -> None: ...
 class BaseAbstractCrawler(BaseCrawler, ABC):
     def __init__(self, scroll_limit: int = 5) -> None:
         options = webdriver.ChromeOptions()
-
+        options.binary_location = "/opt/chrome/chrome"
         options.add_argument("--no-sandbox")
         options.add_argument("--headless=new")
+        options.add_argument("--single-process")
         options.add_argument("--disable-dev-shm-usage")
+        options.add_argument("--disable-gpu")
         options.add_argument("--log-level=3")
         options.add_argument("--disable-popup-blocking")
         options.add_argument("--disable-notifications")
-        options.add_argument("--disable-extensions")
-        options.add_argument("--disable-background-networking")
+        options.add_argument("--disable-dev-tools")
         options.add_argument("--ignore-certificate-errors")
+        options.add_argument("--no-zygote")
         options.add_argument(f"--user-data-dir={mkdtemp()}")
         options.add_argument(f"--data-path={mkdtemp()}")
         options.add_argument(f"--disk-cache-dir={mkdtemp()}")
-        options.add_argument("--remote-debugging-port=9226")
+        options.add_argument("--remote-debugging-port=9222")
 
         self.set_extra_driver_options(options)
 
         self.scroll_limit = scroll_limit
         self.driver = webdriver.Chrome(
+            service=webdriver.ChromeService("/opt/chromedriver"),
             options=options,
         )
 
diff --git a/src/data_crawling/crawlers/github.py b/course/module-1/crawlers/github.py
similarity index 94%
rename from src/data_crawling/crawlers/github.py
rename to course/module-1/crawlers/github.py
index ff69599..ec6fe13 100644
--- a/src/data_crawling/crawlers/github.py
+++ b/course/module-1/crawlers/github.py
@@ -4,11 +4,11 @@
 import tempfile
 
 from aws_lambda_powertools import Logger
-from core.db.documents import RepositoryDocument
 
 from crawlers.base import BaseCrawler
+from documents import RepositoryDocument
 
-logger = Logger(service="llm-twin-course/crawler")
+logger = Logger(service="decodingml/crawler")
 
 
 class GithubCrawler(BaseCrawler):
diff --git a/src/data_crawling/crawlers/linkedin.py b/course/module-1/crawlers/linkedin.py
similarity index 80%
rename from src/data_crawling/crawlers/linkedin.py
rename to course/module-1/crawlers/linkedin.py
index e25f371..dd4547d 100644
--- a/src/data_crawling/crawlers/linkedin.py
+++ b/course/module-1/crawlers/linkedin.py
@@ -5,13 +5,13 @@
 from bs4 import BeautifulSoup
 from bs4.element import Tag
 from config import settings
-from core.db.documents import PostDocument
-from core.errors import ImproperlyConfigured
+from documents import PostDocument
+from errors import ImproperlyConfigured
 from selenium.webdriver.common.by import By
 
 from crawlers.base import BaseAbstractCrawler
 
-logger = Logger(service="llm-twin-course/crawler")
+logger = Logger(service="decodingml/crawler")
 
 
 class LinkedInCrawler(BaseAbstractCrawler):
@@ -21,10 +21,6 @@ def set_extra_driver_options(self, options) -> None:
         options.add_experimental_option("detach", True)
 
     def extract(self, link: str, **kwargs):
-        raise DeprecationWarning(
-            "LinkedIn crawler is deprecated and no longer supported."
-        )
-
         logger.info(f"Starting scrapping data for profile: {link}")
 
         self.login()
@@ -43,7 +39,7 @@ def extract(self, link: str, **kwargs):
         time.sleep(5)
         button = self.driver.find_element(
             By.CSS_SELECTOR,
-            ".app-aware-link.profile-creator-shared-content-view__footer-action",
+            ".app-aware-link.profile-creator-shared-content-view__footer-action"
         )
         button.click()
 
@@ -63,12 +59,7 @@ def extract(self, link: str, **kwargs):
         self.driver.close()
 
         self.model.bulk_insert(
-            [
-                PostDocument(
-                    platform="linkedin", content=post, author_id=kwargs.get("user")
-                )
-                for post in posts
-            ]
+            [PostDocument(platform="linkedin", content=post, author_id=kwargs.get("user")) for post in posts]
         )
 
         logger.info(f"Finished scrapping data for profile: {link}")
@@ -104,9 +95,7 @@ def _get_page_content(self, url: str) -> BeautifulSoup:
         time.sleep(5)
         return BeautifulSoup(self.driver.page_source, "html.parser")
 
-    def _extract_posts(
-        self, post_elements: List[Tag], post_images: Dict[str, str]
-    ) -> Dict[str, Dict[str, str]]:
+    def _extract_posts(self, post_elements: List[Tag], post_images: Dict[str, str]) -> Dict[str, Dict[str, str]]:
         """
         Extracts post texts and combines them with their respective images.
 
@@ -142,23 +131,11 @@ def _scrape_education(self, profile_url: str) -> str:
         return education_content.get_text(strip=True) if education_content else ""
 
     def login(self):
-        raise DeprecationWarning(
-            "LinkedIn crawler is deprecated and no longer supported."
-        )
-
         """Log in to LinkedIn."""
         self.driver.get("https://www.linkedin.com/login")
         if not settings.LINKEDIN_USERNAME and not settings.LINKEDIN_PASSWORD:
-            raise ImproperlyConfigured(
-                "LinkedIn scraper requires an valid account to perform extraction"
-            )
+            raise ImproperlyConfigured("LinkedIn scraper requires an valid account to perform extraction")
 
-        self.driver.find_element(By.ID, "username").send_keys(
-            settings.LINKEDIN_USERNAME
-        )
-        self.driver.find_element(By.ID, "password").send_keys(
-            settings.LINKEDIN_PASSWORD
-        )
-        self.driver.find_element(
-            By.CSS_SELECTOR, ".login__form_action_container button"
-        ).click()
+        self.driver.find_element(By.ID, "username").send_keys(settings.LINKEDIN_USERNAME)
+        self.driver.find_element(By.ID, "password").send_keys(settings.LINKEDIN_PASSWORD)
+        self.driver.find_element(By.CSS_SELECTOR, ".login__form_action_container button").click()
diff --git a/src/data_crawling/crawlers/medium.py b/course/module-1/crawlers/medium.py
similarity index 93%
rename from src/data_crawling/crawlers/medium.py
rename to course/module-1/crawlers/medium.py
index 2369b00..9103daa 100644
--- a/src/data_crawling/crawlers/medium.py
+++ b/course/module-1/crawlers/medium.py
@@ -1,11 +1,11 @@
 from aws_lambda_powertools import Logger
 from bs4 import BeautifulSoup
-from core.db.documents import ArticleDocument
 from selenium.webdriver.common.by import By
 
 from crawlers.base import BaseAbstractCrawler
+from documents import ArticleDocument
 
-logger = Logger(service="llm-twin-course/crawler")
+logger = Logger(service="decodingml/crawler")
 
 
 class MediumCrawler(BaseAbstractCrawler):
diff --git a/course/module-1/db.py b/course/module-1/db.py
new file mode 100644
index 0000000..2617924
--- /dev/null
+++ b/course/module-1/db.py
@@ -0,0 +1,31 @@
+from aws_lambda_powertools import Logger
+from config import settings
+from pymongo import MongoClient
+from pymongo.errors import ConnectionFailure
+
+logger = Logger(service="decodingml/crawler")
+
+
+class MongoDatabaseConnector:
+    _instance: MongoClient | None = None
+
+    def __new__(cls, *args, **kwargs) -> MongoClient:
+        if cls._instance is None:
+            try:
+                cls._instance = MongoClient(settings.DATABASE_HOST)
+            except ConnectionFailure as e:
+                logger.error(f"Couldn't connect to the database: {str(e)}")
+                raise
+
+        logger.info(
+            f"Connection to database with uri: {settings.DATABASE_HOST} successful"
+        )
+        return cls._instance
+
+    def close(self):
+        if self._instance:
+            self._instance.close()
+            logger.info("Connected to database has been closed.")
+
+
+connection = MongoDatabaseConnector()
diff --git a/src/data_crawling/dispatcher.py b/course/module-1/dispatcher.py
similarity index 60%
rename from src/data_crawling/dispatcher.py
rename to course/module-1/dispatcher.py
index 919f9fa..6a0dba2 100644
--- a/src/data_crawling/dispatcher.py
+++ b/course/module-1/dispatcher.py
@@ -1,10 +1,6 @@
 import re
 
-from aws_lambda_powertools import Logger
 from crawlers.base import BaseCrawler
-from crawlers.custom_article import CustomArticleCrawler
-
-logger = Logger(service="llm-twin-course/crawler")
 
 
 class CrawlerDispatcher:
@@ -19,8 +15,4 @@ def get_crawler(self, url: str) -> BaseCrawler:
             if re.match(pattern, url):
                 return crawler()
         else:
-            logger.warning(
-                f"No crawler found for {url}. Defaulting to CustomArticleCrawler."
-            )
-
-            return CustomArticleCrawler()
+            raise ValueError("No crawler found for the provided link")
diff --git a/course/module-1/documents.py b/course/module-1/documents.py
new file mode 100644
index 0000000..506fece
--- /dev/null
+++ b/course/module-1/documents.py
@@ -0,0 +1,122 @@
+import uuid
+from typing import List, Optional
+
+from aws_lambda_powertools import Logger
+from config import settings
+from db import connection
+from errors import ImproperlyConfigured
+from pydantic import UUID4, BaseModel, ConfigDict, Field
+from pymongo import errors
+
+logger = Logger(service="decodingml/crawler")
+_database = connection.get_database(settings.DATABASE_NAME)
+
+
+class BaseDocument(BaseModel):
+    id: UUID4 = Field(default_factory=uuid.uuid4)
+
+    model_config = ConfigDict(from_attributes=True, populate_by_name=True)
+
+    @classmethod
+    def from_mongo(cls, data: dict):
+        """Convert "_id" (str object) into "id" (UUID object)."""
+        if not data:
+            return data
+
+        id = data.pop("_id", None)
+        return cls(**dict(data, id=id))
+
+    def to_mongo(self, **kwargs) -> dict:
+        """Convert "id" (UUID object) into "_id" (str object)."""
+        exclude_unset = kwargs.pop("exclude_unset", False)
+        by_alias = kwargs.pop("by_alias", True)
+
+        parsed = self.model_dump(
+            exclude_unset=exclude_unset, by_alias=by_alias, **kwargs
+        )
+
+        if "_id" not in parsed and "id" in parsed:
+            parsed["_id"] = str(parsed.pop("id"))
+
+        return parsed
+
+    def save(self, **kwargs):
+        collection = _database[self._get_collection_name()]
+        try:
+            result = collection.insert_one(self.to_mongo(**kwargs))
+            return result.inserted_id
+        except errors.WriteError as e:
+            logger.error(f"Failed to insert document {e}")
+            return None
+
+    @classmethod
+    def get_or_create(cls, **filter_options) -> Optional[str]:
+        collection = _database[cls._get_collection_name()]
+        try:
+            instance = collection.find_one(filter_options)
+            if instance:
+                return str(cls.from_mongo(instance).id)
+            new_instance = cls(**filter_options)
+            new_instance = new_instance.save()
+            return new_instance
+        except errors.OperationFailure as e:
+            logger.error(f"Failed to retrieve document: {e}")
+            return None
+
+    @classmethod
+    def bulk_insert(cls, documents: List, **kwargs) -> Optional[List[str]]:
+        collection = _database[cls._get_collection_name()]
+        try:
+            result = collection.insert_many(
+                [doc.to_mongo(**kwargs) for doc in documents]
+            )
+            return result.inserted_ids
+        except errors.WriteError as e:
+            logger.error(f"Failed to insert document {e}")
+            return None
+
+    @classmethod
+    def _get_collection_name(cls):
+        if not hasattr(cls, "Settings") or not hasattr(cls.Settings, "name"):
+            raise ImproperlyConfigured(
+                "Document should define an Settings configuration class with the name of the collection."
+            )
+
+        return cls.Settings.name
+
+
+class UserDocument(BaseDocument):
+    first_name: str
+    last_name: str
+
+    class Settings:
+        name = "users"
+
+
+class RepositoryDocument(BaseDocument):
+    name: str
+    link: str
+    content: dict
+    owner_id: str = Field(alias="owner_id")
+
+    class Settings:
+        name = "repositories"
+
+
+class PostDocument(BaseDocument):
+    platform: str
+    content: dict
+    author_id: str = Field(alias="author_id")
+
+    class Settings:
+        name = "posts"
+
+
+class ArticleDocument(BaseDocument):
+    platform: str
+    link: str
+    content: dict
+    author_id: str = Field(alias="author_id")
+
+    class Settings:
+        name = "articles"
diff --git a/course/module-1/errors.py b/course/module-1/errors.py
new file mode 100644
index 0000000..5478443
--- /dev/null
+++ b/course/module-1/errors.py
@@ -0,0 +1,6 @@
+class ScrabbleException(Exception):
+    pass
+
+
+class ImproperlyConfigured(ScrabbleException):
+    pass
diff --git a/src/core/lib.py b/course/module-1/lib.py
similarity index 60%
rename from src/core/lib.py
rename to course/module-1/lib.py
index 45cca69..c8c64bb 100644
--- a/src/core/lib.py
+++ b/course/module-1/lib.py
@@ -1,7 +1,7 @@
-from core.errors import ImproperlyConfigured
+from errors import ImproperlyConfigured
 
 
-def split_user_full_name(user: str | None) -> tuple[str, str]:
+def user_to_names(user: str | None) -> tuple[str, str]:
     if user is None:
         raise ImproperlyConfigured("User name is empty")
 
@@ -14,9 +14,3 @@ def split_user_full_name(user: str | None) -> tuple[str, str]:
         first_name, last_name = " ".join(name_tokens[:-1]), name_tokens[-1]
 
     return first_name, last_name
-
-
-def flatten(nested_list: list) -> list:
-    """Flatten a list of lists into a single list."""
-
-    return [item for sublist in nested_list for item in sublist]
diff --git a/src/data_crawling/main.py b/course/module-1/main.py
similarity index 58%
rename from src/data_crawling/main.py
rename to course/module-1/main.py
index cf3b822..aec81ac 100644
--- a/src/data_crawling/main.py
+++ b/course/module-1/main.py
@@ -1,30 +1,30 @@
 from typing import Any
 
+import lib
 from aws_lambda_powertools import Logger
 from aws_lambda_powertools.utilities.typing import LambdaContext
-from core import lib
-from core.db.documents import UserDocument
-from crawlers import CustomArticleCrawler, GithubCrawler, LinkedInCrawler
+from crawlers import GithubCrawler, LinkedInCrawler, MediumCrawler
 from dispatcher import CrawlerDispatcher
+from documents import UserDocument
 
-logger = Logger(service="llm-twin-course/crawler")
+logger = Logger(service="decodingml/crawler")
 
 _dispatcher = CrawlerDispatcher()
-_dispatcher.register("medium", CustomArticleCrawler)
+_dispatcher.register("medium", MediumCrawler)
 _dispatcher.register("linkedin", LinkedInCrawler)
 _dispatcher.register("github", GithubCrawler)
 
 
-def handler(event, context: LambdaContext | None = None) -> dict[str, Any]:
-    first_name, last_name = lib.split_user_full_name(event.get("user"))
+def handler(event, context: LambdaContext) -> dict[str, Any]:
+    first_name, last_name = lib.user_to_names(event.get("user"))
 
-    user_id = UserDocument.get_or_create(first_name=first_name, last_name=last_name)
+    user = UserDocument.get_or_create(first_name=first_name, last_name=last_name)
 
     link = event.get("link")
     crawler = _dispatcher.get_crawler(link)
 
     try:
-        crawler.extract(link=link, user=user_id)
+        crawler.extract(link=link, user=user)
 
         return {"statusCode": 200, "body": "Link processed successfully"}
     except Exception as e:
diff --git a/course/module-1/poetry.lock b/course/module-1/poetry.lock
new file mode 100644
index 0000000..f527b3c
--- /dev/null
+++ b/course/module-1/poetry.lock
@@ -0,0 +1,2010 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.3"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"},
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"},
+    {file = "aiohttp-3.9.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:82e6aa28dd46374f72093eda8bcd142f7771ee1eb9d1e223ff0fa7177a96b4a5"},
+    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f56455b0c2c7cc3b0c584815264461d07b177f903a04481dfc33e08a89f0c26b"},
+    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bca77a198bb6e69795ef2f09a5f4c12758487f83f33d63acde5f0d4919815768"},
+    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e083c285857b78ee21a96ba1eb1b5339733c3563f72980728ca2b08b53826ca5"},
+    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab40e6251c3873d86ea9b30a1ac6d7478c09277b32e14745d0d3c6e76e3c7e29"},
+    {file = "aiohttp-3.9.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:df822ee7feaaeffb99c1a9e5e608800bd8eda6e5f18f5cfb0dc7eeb2eaa6bbec"},
+    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:acef0899fea7492145d2bbaaaec7b345c87753168589cc7faf0afec9afe9b747"},
+    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:cd73265a9e5ea618014802ab01babf1940cecb90c9762d8b9e7d2cc1e1969ec6"},
+    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:a78ed8a53a1221393d9637c01870248a6f4ea5b214a59a92a36f18151739452c"},
+    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:6b0e029353361f1746bac2e4cc19b32f972ec03f0f943b390c4ab3371840aabf"},
+    {file = "aiohttp-3.9.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7cf5c9458e1e90e3c390c2639f1017a0379a99a94fdfad3a1fd966a2874bba52"},
+    {file = "aiohttp-3.9.3-cp310-cp310-win32.whl", hash = "sha256:3e59c23c52765951b69ec45ddbbc9403a8761ee6f57253250c6e1536cacc758b"},
+    {file = "aiohttp-3.9.3-cp310-cp310-win_amd64.whl", hash = "sha256:055ce4f74b82551678291473f66dc9fb9048a50d8324278751926ff0ae7715e5"},
+    {file = "aiohttp-3.9.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6b88f9386ff1ad91ace19d2a1c0225896e28815ee09fc6a8932fded8cda97c3d"},
+    {file = "aiohttp-3.9.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c46956ed82961e31557b6857a5ca153c67e5476972e5f7190015018760938da2"},
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+    {file = "yarl-1.9.4-cp38-cp38-win32.whl", hash = "sha256:686a0c2f85f83463272ddffd4deb5e591c98aac1897d65e92319f729c320eece"},
+    {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"},
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+    {file = "yarl-1.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ba6f52cbc7809cd8d74604cce9c14868306ae4aa0282016b641c661f981a6e91"},
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+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"},
+    {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"},
+    {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"},
+    {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "327d0b77a0bf7f3897a60e4f5abc41e5d2df705b2490df5d3645b6f0de44cd17"
diff --git a/course/module-1/pyproject.toml b/course/module-1/pyproject.toml
new file mode 100644
index 0000000..3510720
--- /dev/null
+++ b/course/module-1/pyproject.toml
@@ -0,0 +1,24 @@
+[tool.poetry]
+name = "module_1"
+version = "0.1.0"
+description = ""
+authors = ["Deocding ML"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+pymongo = "^4.6.2"
+pydantic = "^2.6.4"
+pydantic-settings = "^2.2.1"
+selenium = "^4.18.1"
+webdriver-manager = "^4.0.1"
+aws-lambda-powertools = "^2.35.1"
+langchain = "0.1.11"
+langchain-openai = ">=0.0.2"
+beautifulsoup4 = "^4.12.3"
+jmespath = "^1.0.1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/course/module-2/.docker/Dockerfile.cdc b/course/module-2/.docker/Dockerfile.cdc
new file mode 100644
index 0000000..493d01e
--- /dev/null
+++ b/course/module-2/.docker/Dockerfile.cdc
@@ -0,0 +1,24 @@
+# Use an official Python runtime as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /usr/src/app
+
+# Upgrade pip and install Poetry
+RUN python3 -m pip install --upgrade pip && pip3 install poetry
+
+# Copy only the pyproject.toml and poetry.lock (if exists) to use Docker cache
+COPY pyproject.toml poetry.lock* /usr/src/app/
+
+# Install project dependencies
+RUN poetry config virtualenvs.create false && \
+    poetry install --no-interaction --no-ansi
+
+# Copy the rest of the application
+COPY . /usr/src/app
+
+# Make port 8000 available to the world outside this container
+EXPOSE 8000
+
+# Run cdc.py when the container launches
+CMD ["python", "cdc.py"]
diff --git a/course/module-2/.env.example b/course/module-2/.env.example
new file mode 100644
index 0000000..659b480
--- /dev/null
+++ b/course/module-2/.env.example
@@ -0,0 +1,9 @@
+#MongoDB Config    
+MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+MONGO_DATABASE_NAME: str = "admin"
+
+#RabbitMQ Config
+RABBITMQ_HOST: str = "localhost"  # or the Docker host if running remotely
+RABBITMQ_PORT: int = 5673  # Port mapped in Docker Compose
+RABBITMQ_DEFAULT_USERNAME: str = "guest"  # Default username
+RABBITMQ_DEFAULT_PASSWORD: str = "guest"  # Default password
diff --git a/course/module-2/.gitignore b/course/module-2/.gitignore
new file mode 100644
index 0000000..338e574
--- /dev/null
+++ b/course/module-2/.gitignore
@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+data/
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
\ No newline at end of file
diff --git a/course/module-2/Makefile b/course/module-2/Makefile
new file mode 100644
index 0000000..4c2b0a7
--- /dev/null
+++ b/course/module-2/Makefile
@@ -0,0 +1,11 @@
+help:
+	@grep -E '^[a-zA-Z0-9 -]+:.*#'  Makefile | sort | while read -r l; do printf "\033[1;32m$$(echo $$l | cut -f 1 -d':')\033[00m:$$(echo $$l | cut -f 2- -d'#')\n"; done
+
+local-start: # Buil and start mongodb and mq.
+	docker-compose -f docker-compose.yml up --build -d
+
+local-start-cdc: # Start CDC system
+	python cdc.py
+
+local-test-cdc: #Test CDC system by inserting data to mongodb
+	python test_cdc.py
\ No newline at end of file
diff --git a/course/module-2/Readme.md b/course/module-2/Readme.md
new file mode 100644
index 0000000..d2e05f0
--- /dev/null
+++ b/course/module-2/Readme.md
@@ -0,0 +1,115 @@
+# Module 2: Change Data Capture: Enabling Event-Driven Architecture
+
+
+# Introduction
+Change Data Capture, commonly known as CDC, is an efficient way to track changes in a database.
+
+The purpose of CDC is to capture insertions, updates, and deletions applied to a database and to make this change data available in a format easily consumable by downstream applications.
+Why do we need CDC pattern?
+
+    1. Real-time Data Syncing: CDC facilitates near-real-time data integration and syncing.
+    2. Efficient Data Pipelines: It allows incremental data loading, which is more efficient than bulk load operations.
+    3. Minimized System Impact: CDC minimizes the impact on the source system by reducing the need for performance-intensive queries.
+    4. Event-Driven Architectures: It enables event-driven architectures by streaming database events.
+
+
+# The module structure
+## Data Flow Module
+Singleton class to manage RabbitMQ connection.
+- `mq.py`
+
+## DB Module
+Singleton class to connect to MongoDB database.
+- `mongo.py`
+
+## cdc.py
+- **Functionality**: This script sets up a CDC pipeline. It monitors a MongoDB database for changes and forwards these changes to a RabbitMQ message queue.
+- **MongoDB Connection**: It connects to a MongoDB database (specifically the "scrabble" collection), utilizing a class from `db.mongo`.
+- **Change Tracking**: The script watches for insert operations in MongoDB. When a change is detected, it processes and serializes the change details.
+- **Data Processing**: Each change's metadata is extracted and serialized using `json_util`. The `_id` field is converted to a string and additional metadata is added.
+- **RabbitMQ Integration**: After serialization, the script publishes the data to a RabbitMQ queue named "test_queue".
+
+## test_cdc.py
+This script is designed to test the MongoDB database's data insertion, which is integral to the CDC process.
+
+- **Purpose**: Validates that new data entries are correctly inserted and detected by the CDC setup.
+- **Function**: Includes `insert_data_to_mongodb` function to connect and insert data into MongoDB.
+- **Usage**: It connects to MongoDB using a specified URI, inserts test data into a defined database and collection, and handles exceptions.
+- **Testing Data**: Utilizes test data to simulate real insertion operations, aiding in verifying the overall functionality of the CDC system.
+
+The script ensures that the CDC pipeline correctly interacts with MongoDB, acting as a part of the testing suite for the system.
+
+# Installation and Setup
+To set up the environment for this project, follow these steps:
+
+1. **Install Dependencies**: Run `poetry install` to install the required dependencies.
+2. **Environment Variables**: Use the `.env.example` file as a template to create your `.env` file. This file should include configurations for MongoDB and RabbitMQ, like the host, port, and credentials.
+   
+   Example variables:
+   - MongoDB Configuration (MONGO_DATABASE_HOST, MONGO_DATABASE_NAME)
+   - RabbitMQ Configuration (RABBITMQ_HOST, RABBITMQ_PORT, RABBITMQ_DEFAULT_USERNAME, RABBITMQ_DEFAULT_PASSWORD)
+
+Ensure you replace the example values with your specific configuration details.
+
+# Docker Configuration
+The project includes a `docker-compose.yaml` file to manage the Docker containers for MongoDB and RabbitMQ. Here’s an overview of the configuration:
+
+- **MongoDB Replica Set**: Configures three MongoDB containers (`mongo1`, `mongo2`, `mongo3`) as a replica set for redundancy and high availability.
+- **RabbitMQ**: Sets up a RabbitMQ container with management capabilities.
+- **Volumes and Ports**: Defines specific ports and volumes for each service to ensure persistent storage and accessibility.
+- **Healthcheck**: Includes a healthcheck for MongoDB to ensure the containers are functioning correctly.
+
+# Usage
+
+The project includes a Makefile for easy management of common tasks. Here are the main commands you can use:
+
+- `make help`: Displays help for each make command.
+- `make local-start`: Builds and starts MongoDB and RabbitMQ using Docker. This prepares the necessary services for the CDC system.
+- `make local-start-cdc`: Starts the CDC system by running the `cdc.py` script.
+- `make local-test-cdc`: Runs the `test_cdc.py` script to insert test data into MongoDB, testing the CDC system's response.
+
+# Cloud environment
+TBD
+
+
+
+# Meet your teachers!
+The course is created under the [Decoding ML](https://decodingml.substack.com/) umbrella by:
+
+<table>
+  <tr>
+    <td><a href="https://github.com/iusztinpaul" target="_blank"><img src="https://github.com/iusztinpaul.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Paul Iusztin</strong><br />
+      <i>Senior ML & MLOps Engineer</i>
+    </td>
+  </tr>
+  <tr>
+    <td><a href="https://github.com/alexandruvesa" target="_blank"><img src="https://github.com/alexandruvesa.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Alexandru Vesa</strong><br />
+      <i>Senior AI Engineer</i>
+    </td>
+  </tr>
+  <tr>
+    <td><a href="https://github.com/Joywalker" target="_blank"><img src="https://github.com/Joywalker.png" width="100" style="border-radius:50%;"/></a></td>
+    <td>
+      <strong>Răzvanț Alexandru</strong><br />
+      <i>Senior ML Engineer</i>
+    </td>
+  </tr>
+</table>
+
+# License
+
+This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).
+
+# 🏆 Contribution
+
+A big "Thank you 🙏" to all our contributors! This course is possible only because of their efforts.
+
+<p align="center">
+    <a href="https://github.com/decodingml/llm-twin-course/graphs/contributors">
+      <img src="https://contrib.rocks/image?repo=decodingml/llm-twin-course" />
+    </a>
+</p>
diff --git a/src/data_cdc/cdc.py b/course/module-2/cdc.py
similarity index 55%
rename from src/data_cdc/cdc.py
rename to course/module-2/cdc.py
index 249181a..6db8aac 100644
--- a/src/data_cdc/cdc.py
+++ b/course/module-2/cdc.py
@@ -2,18 +2,21 @@
 import logging
 
 from bson import json_util
-from config import settings
-from core.db.mongo import MongoDatabaseConnector
-from core.logger_utils import get_logger
-from core.mq import publish_to_rabbitmq
 
-logger = get_logger(__file__)
+from data_flow.mq import publish_to_rabbitmq
+from db.mongo import MongoDatabaseConnector
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
 
 
 def stream_process():
     try:
+        # Setup MongoDB connection
         client = MongoDatabaseConnector()
-        db = client["twin"]
+        db = client["scrabble"]
         logging.info("Connected to MongoDB.")
 
         # Watch changes in a specific collection
@@ -21,27 +24,20 @@ def stream_process():
         for change in changes:
             data_type = change["ns"]["coll"]
             entry_id = str(change["fullDocument"]["_id"])  # Convert ObjectId to string
-
             change["fullDocument"].pop("_id")
             change["fullDocument"]["type"] = data_type
             change["fullDocument"]["entry_id"] = entry_id
 
-            if data_type not in ["articles", "posts", "repositories"]:
-                logging.info(f"Unsupported data type: '{data_type}'")
-                continue
-
             # Use json_util to serialize the document
             data = json.dumps(change["fullDocument"], default=json_util.default)
-            logger.info(
-                f"Change detected and serialized for a data sample of type {data_type}."
-            )
+            logging.info(f"Change detected and serialized: {data}")
 
             # Send data to rabbitmq
-            publish_to_rabbitmq(queue_name=settings.RABBITMQ_QUEUE_NAME, data=data)
-            logger.info(f"Data of type '{data_type}' published to RabbitMQ.")
+            publish_to_rabbitmq(queue_name="test_queue", data=data)
+            logging.info("Data published to RabbitMQ.")
 
     except Exception as e:
-        logger.error(f"An error occurred: {e}")
+        logging.error(f"An error occurred: {e}")
 
 
 if __name__ == "__main__":
diff --git a/src/core/mq.py b/course/module-2/data_flow/mq.py
similarity index 81%
rename from src/core/mq.py
rename to course/module-2/data_flow/mq.py
index 9f65159..f255cdd 100644
--- a/src/core/mq.py
+++ b/course/module-2/data_flow/mq.py
@@ -1,11 +1,6 @@
-from typing import Self
-
 import pika
-from config import settings
-
-from core.logger_utils import get_logger
 
-logger = get_logger(__file__)
+from settings import settings
 
 
 class RabbitMQConnection:
@@ -13,22 +8,28 @@ class RabbitMQConnection:
 
     _instance = None
 
-    def __new__(cls, *args, **kwargs) -> Self:
+    def __new__(
+        cls,
+        host: str = None,
+        port: int = None,
+        username: str = None,
+        password: str = None,
+        virtual_host: str = "/",
+    ):
         if not cls._instance:
-            cls._instance = super().__new__(cls, *args, **kwargs)
-
+            cls._instance = super().__new__(cls)
         return cls._instance
 
     def __init__(
         self,
-        host: str | None = None,
-        port: int | None = None,
-        username: str | None = None,
-        password: str | None = None,
+        host: str = None,
+        port: int = None,
+        username: str = None,
+        password: str = None,
         virtual_host: str = "/",
         fail_silently: bool = False,
         **kwargs,
-    ) -> None:
+    ):
         self.host = host or settings.RABBITMQ_HOST
         self.port = port or settings.RABBITMQ_PORT
         self.username = username or settings.RABBITMQ_DEFAULT_USERNAME
@@ -56,7 +57,7 @@ def connect(self):
                 )
             )
         except pika.exceptions.AMQPConnectionError as e:
-            logger.exception("Failed to connect to RabbitMQ:")
+            print("Failed to connect to RabbitMQ:", e)
             if not self.fail_silently:
                 raise e
 
@@ -99,10 +100,11 @@ def publish_to_rabbitmq(queue_name: str, data: str):
                     delivery_mode=2,  # make message persistent
                 ),
             )
+            print("Sent data to RabbitMQ:", data)
     except pika.exceptions.UnroutableError:
-        logger.warning("Message could not be routed")
-    except Exception:
-        logger.exception("Error publishing to RabbitMQ.")
+        print("Message could not be routed")
+    except Exception as e:
+        print(f"Error publishing to RabbitMQ: {e}")
 
 
 if __name__ == "__main__":
diff --git a/src/core/db/mongo.py b/course/module-2/db/mongo.py
similarity index 52%
rename from src/core/db/mongo.py
rename to course/module-2/db/mongo.py
index dfda473..ea148dc 100644
--- a/src/core/db/mongo.py
+++ b/course/module-2/db/mongo.py
@@ -1,40 +1,35 @@
 from pymongo import MongoClient
 from pymongo.errors import ConnectionFailure
 
-from core.config import settings
-from core.logger_utils import get_logger
-
-logger = get_logger(__file__)
+from settings import settings
 
 
 class MongoDatabaseConnector:
     """Singleton class to connect to MongoDB database."""
 
-    _instance: MongoClient | None = None
+    _instance: MongoClient = None
 
     def __new__(cls, *args, **kwargs):
         if cls._instance is None:
             try:
                 cls._instance = MongoClient(settings.MONGO_DATABASE_HOST)
-                logger.info(
-                    f"Connection to database with uri: {settings.MONGO_DATABASE_HOST} successful"
-                )
-            except ConnectionFailure:
-                logger.error(f"Couldn't connect to the database.")
-
+                # cls._instance = MongoClient("mongodb://localhost:30001/?replicaSet=my-replica-set")
+            except ConnectionFailure as e:
+                print(f"Couldn't connect to the database: {str(e)}")
                 raise
 
+        print(
+            f"Connection to database with uri: {settings.MONGO_DATABASE_HOST} successful"
+        )
         return cls._instance
 
     def get_database(self):
-        assert self._instance, "Database connection not initialized"
-
         return self._instance[settings.MONGO_DATABASE_NAME]
 
     def close(self):
         if self._instance:
             self._instance.close()
-            logger.info("Connected to database has been closed.")
+            print("Connected to database has been closed.")
 
 
 connection = MongoDatabaseConnector()
diff --git a/course/module-2/docker-compose.yml b/course/module-2/docker-compose.yml
new file mode 100644
index 0000000..a2723e2
--- /dev/null
+++ b/course/module-2/docker-compose.yml
@@ -0,0 +1,44 @@
+version: '3.8'
+
+services:
+  mongo1:
+    image: mongo:5
+    container_name: mongo1
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30001"]
+    volumes:
+      - ./data/mongo-1:/data/db
+    ports:
+      - 30001:30001
+    healthcheck:
+      test: test $$(echo "rs.initiate({_id:'my-replica-set',members:[{_id:0,host:\"mongo1:30001\"},{_id:1,host:\"mongo2:30002\"},{_id:2,host:\"mongo3:30003\"}]}).ok || rs.status().ok" | mongo --port 30001 --quiet) -eq 1
+      interval: 10s
+      start_period: 30s
+
+  mongo2:
+    image: mongo:5
+    container_name: mongo2
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30002"]
+    volumes:
+      - ./data/mongo-2:/data/db
+    ports:
+      - 30002:30002
+
+  mongo3:
+    image: mongo:5
+    container_name: mongo3
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30003"]
+    volumes:
+      - ./data/mongo-3:/data/db
+    ports:
+      - 30003:30003
+
+  mq:
+    image: rabbitmq:3-management-alpine
+    container_name: scrabble_mq
+    ports:
+      - "5673:5672"
+      - "15673:15672"
+    volumes:
+      - ~/rabbitmq/data/:/var/lib/rabbitmq/
+      - ~/rabbitmq/log/:/var/log/rabbitmq
+    restart: always
diff --git a/course/module-2/poetry.lock b/course/module-2/poetry.lock
new file mode 100644
index 0000000..60fc868
--- /dev/null
+++ b/course/module-2/poetry.lock
@@ -0,0 +1,310 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "annotated-types"
+version = "0.6.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
+    {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+]
+
+[[package]]
+name = "dnspython"
+version = "2.6.1"
+description = "DNS toolkit"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "dnspython-2.6.1-py3-none-any.whl", hash = "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50"},
+    {file = "dnspython-2.6.1.tar.gz", hash = "sha256:e8f0f9c23a7b7cb99ded64e6c3a6f3e701d78f50c55e002b839dea7225cff7cc"},
+]
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+dnssec = ["cryptography (>=41)"]
+doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
+doq = ["aioquic (>=0.9.25)"]
+idna = ["idna (>=3.6)"]
+trio = ["trio (>=0.23)"]
+wmi = ["wmi (>=1.5.1)"]
+
+[[package]]
+name = "pika"
+version = "1.3.2"
+description = "Pika Python AMQP Client Library"
+optional = false
+python-versions = ">=3.7"
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+[package.extras]
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+tornado = ["tornado"]
+twisted = ["twisted"]
+
+[[package]]
+name = "pydantic"
+version = "2.6.4"
+description = "Data validation using Python type hints"
+optional = false
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+    {file = "pymongo-4.6.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:0182899aafe830f25cf96c5976d724efeaaf7b6646c15424ad8dd25422b2efe1"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:8d0ea740a2faa56f930dc82c5976d96c017ece26b29a1cddafb58721c7aab960"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux2014_ppc64le.whl", hash = "sha256:5c8a4982f5eb767c6fbfb8fb378683d09bcab7c3251ba64357eef600d43f6c23"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux2014_s390x.whl", hash = "sha256:becfa816545a48c8e740ac2fd624c1c121e1362072d68ffcf37a6b1be8ea187e"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:ff7d1f449fcad23d9bc8e8dc2b9972be38bcd76d99ea5f7d29b2efa929c2a7ff"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e097f877de4d6af13a33ef938bf2a2350f424be5deabf8b857da95f5b080487a"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:705a9bfd619301ee7e985d6f91f68b15dfcb2f6f36b8cc225cc82d4260d2bce5"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2ef1b4992ee1cb8bb16745e70afa0c02c5360220a7a8bb4775888721f052d0a6"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3d10bdd46cbc35a2109737d36ffbef32e7420569a87904738ad444ccb7ac2c5"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:17c1c143ba77d6e21fc8b48e93f0a5ed982a23447434e9ee4fbb6d633402506b"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9e51e30d67b468a2a634ade928b30cb3e420127f148a9aec60de33f39087bdc4"},
+    {file = "pymongo-4.6.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:bec8e4e88984be157408f1923d25869e1b575c07711cdbdde596f66931800934"},
+    {file = "pymongo-4.6.3-cp39-cp39-win32.whl", hash = "sha256:98877a9c4ad42df8253a12d8d17a3265781d1feb5c91c767bd153f88feb0b670"},
+    {file = "pymongo-4.6.3-cp39-cp39-win_amd64.whl", hash = "sha256:6d5b35da9e16cda630baed790ffc3d0d01029d269523a7cec34d2ec7e6823e75"},
+    {file = "pymongo-4.6.3.tar.gz", hash = "sha256:400074090b9a631f120b42c61b222fd743490c133a5d2f99c0208cefcccc964e"},
+]
+
+[package.dependencies]
+dnspython = ">=1.16.0,<3.0.0"
+
+[package.extras]
+aws = ["pymongo-auth-aws (<2.0.0)"]
+encryption = ["certifi", "pymongo[aws]", "pymongocrypt (>=1.6.0,<2.0.0)"]
+gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
+ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
+snappy = ["python-snappy"]
+test = ["pytest (>=7)"]
+zstd = ["zstandard"]
+
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
+    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.10.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "1c0a726665bbc4408c0fb364da162006359dd102665d3f1d276c7622c5141c54"
diff --git a/course/module-2/pyproject.toml b/course/module-2/pyproject.toml
new file mode 100644
index 0000000..7706696
--- /dev/null
+++ b/course/module-2/pyproject.toml
@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "cdc"
+version = "0.1.0"
+description = ""
+authors = ["Vesa Alexandru"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+pymongo = "^4.6.3"
+pydantic-settings = "^2.2.1"
+pika = "^1.3.2"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/course/module-2/settings.py b/course/module-2/settings.py
new file mode 100644
index 0000000..3b0265b
--- /dev/null
+++ b/course/module-2/settings.py
@@ -0,0 +1,15 @@
+from pydantic_settings import BaseSettings
+
+
+class AppSettings(BaseSettings):
+    # MongoDB configs
+    MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+    MONGO_DATABASE_NAME: str = "admin"
+
+    RABBITMQ_HOST: str = "localhost"  # or the Docker host if running remotely
+    RABBITMQ_PORT: int = 5673  # Port mapped in Docker Compose
+    RABBITMQ_DEFAULT_USERNAME: str = "guest"  # Default username
+    RABBITMQ_DEFAULT_PASSWORD: str = "guest"  # Default password
+
+
+settings = AppSettings()
diff --git a/src/data_cdc/test_cdc.py b/course/module-2/test_cdc.py
similarity index 70%
rename from src/data_cdc/test_cdc.py
rename to course/module-2/test_cdc.py
index fab02a9..7426bf5 100644
--- a/src/data_cdc/test_cdc.py
+++ b/course/module-2/test_cdc.py
@@ -24,9 +24,8 @@ def insert_data_to_mongodb(uri, database_name, collection_name, data):
 
 
 if __name__ == "__main__":
-    insert_data_to_mongodb(
-        "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set",
-        "twin",
-        "posts",
-        {"platform": "linkedin", "content": "Test content"}
-    )
+    mongodb_uri = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+    database_name = "scrabble"
+    collection_name = "test"
+    test_data = {"name": "LLM TWIN"}
+    insert_data_to_mongodb(mongodb_uri, database_name, collection_name, test_data)
diff --git a/course/module-3/.env.example b/course/module-3/.env.example
new file mode 100644
index 0000000..6c0264c
--- /dev/null
+++ b/course/module-3/.env.example
@@ -0,0 +1,24 @@
+# Ingestion embedding model config
+EMBEDDING_MODEL_ID="sentence-transformers/all-MiniLM-L6-v2"
+EMBEDDING_MODEL_MAX_INPUT_LENGTH=256
+EMBEDDING_SIZE=384
+EMBEDDING_MODEL_DEVICE="cpu"
+
+# MongoDB configs
+MONGO_DATABASE_HOST="mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
+MONGO_DATABASE_NAME="scrabble"
+
+# QdrantDB config
+QDRANT_DATABASE_HOST="localhost"
+QDRANT_DATABASE_PORT=6333
+CLEANED_DATA_OUTPUT_COLLECTION_NAME="cleaned_posts"
+QDRANT_APIKEY=
+
+# MQ config
+RABBITMQ_DEFAULT_USERNAME="guest"
+RABBITMQ_DEFAULT_PASSWORD="guest"
+RABBITMQ_HOST="localhost"
+RABBITMQ_PORT= 5673
+
+# Retrieval config
+OPENAI_API_KEY="str"
diff --git a/course/module-3/.gitignore b/course/module-3/.gitignore
new file mode 100644
index 0000000..338e574
--- /dev/null
+++ b/course/module-3/.gitignore
@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+data/
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
\ No newline at end of file
diff --git a/course/module-3/Makefile b/course/module-3/Makefile
new file mode 100644
index 0000000..f5272c2
--- /dev/null
+++ b/course/module-3/Makefile
@@ -0,0 +1,20 @@
+help:
+	@grep -E '^[a-zA-Z0-9 -]+:.*#'  Makefile | sort | while read -r l; do printf "\033[1;32m$$(echo $$l | cut -f 1 -d':')\033[00m:$$(echo $$l | cut -f 2- -d'#')\n"; done
+
+local-start-infra: # Buil and start mongodb, mq and qdrant.
+	docker-compose -f docker-compose.yml up --build -d
+
+local-stop-infra: # Stop mongodb, mq and qdrant.
+	docker-compose -f docker-compose.yml down
+
+local-start-cdc: # Start CDC system
+	poetry run python cdc.py
+
+local-insert-data-mongo: #Insert data to mongodb
+	poetry run python insert_data_mongo.py
+
+local-bytewax: # Run bytewax pipeline
+	poetry run python -m bytewax.run data_flow/bytewax_pipeline
+
+local-test-retriever: # Test retriever
+	poetry run python retriever.py
\ No newline at end of file
diff --git a/course/module-3/README.md b/course/module-3/README.md
new file mode 100644
index 0000000..2fd9f95
--- /dev/null
+++ b/course/module-3/README.md
@@ -0,0 +1 @@
+TBD
\ No newline at end of file
diff --git a/course/module-3/cdc.py b/course/module-3/cdc.py
new file mode 100644
index 0000000..1ed5256
--- /dev/null
+++ b/course/module-3/cdc.py
@@ -0,0 +1,33 @@
+import json
+
+from bson import json_util
+
+from data_flow.mq import RabbitMQConnection
+from db.mongo import MongoDatabaseConnector
+
+
+def stream_process():
+    mq_connection = RabbitMQConnection()
+    mq_connection.connect()
+
+    client = MongoDatabaseConnector()
+
+    db = client["scrabble"]
+
+    changes = db.watch(
+        [{"$match": {"operationType": {"$in": ["insert"]}}}]
+    )  # Filter for inserts only
+    for change in changes:
+        data_type = change["ns"]["coll"]
+        entry_id = str(change["fullDocument"]["_id"])  # Convert ObjectId to string
+        change["fullDocument"].pop("_id")
+        change["fullDocument"]["type"] = data_type
+        change["fullDocument"]["entry_id"] = entry_id
+
+        # Use json_util to serialize the document
+        data = json.dumps(change["fullDocument"], default=json_util.default)
+        mq_connection.publish_message(data=data, queue="mongo_data")
+
+
+if __name__ == "__main__":
+    stream_process()
diff --git a/src/bonus_superlinked_rag/data_flow/__init__.py b/course/module-3/data_flow/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/data_flow/__init__.py
rename to course/module-3/data_flow/__init__.py
diff --git a/src/feature_pipeline/main.py b/course/module-3/data_flow/bytewax_pipeline.py
similarity index 91%
rename from src/feature_pipeline/main.py
rename to course/module-3/data_flow/bytewax_pipeline.py
index 2e01794..41ec87a 100644
--- a/src/feature_pipeline/main.py
+++ b/course/module-3/data_flow/bytewax_pipeline.py
@@ -1,6 +1,5 @@
 import bytewax.operators as op
 from bytewax.dataflow import Dataflow
-from core.db.qdrant import QdrantDatabaseConnector
 from data_flow.stream_input import RabbitMQSource
 from data_flow.stream_output import QdrantOutput
 from data_logic.dispatchers import (
@@ -9,8 +8,7 @@
     EmbeddingDispatcher,
     RawDispatcher,
 )
-
-connection = QdrantDatabaseConnector()
+from db.qdrant import connection
 
 flow = Dataflow("Streaming ingestion pipeline")
 stream = op.input("input", flow, RabbitMQSource())
diff --git a/src/bonus_superlinked_rag/mq.py b/course/module-3/data_flow/mq.py
similarity index 66%
rename from src/bonus_superlinked_rag/mq.py
rename to course/module-3/data_flow/mq.py
index de959fc..7f47130 100644
--- a/src/bonus_superlinked_rag/mq.py
+++ b/course/module-3/data_flow/mq.py
@@ -1,9 +1,6 @@
 import pika
 
-from utils.logging import get_logger
-from config import settings
-
-logger = get_logger(__name__)
+from settings import settings
 
 
 class RabbitMQConnection:
@@ -11,23 +8,22 @@ class RabbitMQConnection:
 
     def __new__(
         cls,
-        host: str | None = None,
-        port: int | None = None,
-        username: str | None = None,
-        password: str | None = None,
+        host: str = None,
+        port: int = None,
+        username: str = None,
+        password: str = None,
         virtual_host: str = "/",
     ):
         if not cls._instance:
             cls._instance = super().__new__(cls)
-
         return cls._instance
 
     def __init__(
         self,
-        host: str | None = None,
-        port: int | None = None,
-        username: str | None = None,
-        password: str | None = None,
+        host: str = None,
+        port: int = None,
+        username: str = None,
+        password: str = None,
         virtual_host: str = "/",
         fail_silently: bool = False,
         **kwargs,
@@ -47,9 +43,7 @@ def __enter__(self):
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.close()
 
-    def connect(self) -> None:
-        logger.info("Trying to connect to RabbitMQ.", host=self.host, port=self.port)
-        
+    def connect(self):
         try:
             credentials = pika.PlainCredentials(self.username, self.password)
             self._connection = pika.BlockingConnection(
@@ -61,12 +55,11 @@ def connect(self) -> None:
                 )
             )
         except pika.exceptions.AMQPConnectionError as e:
-            logger.warning("Failed to connect to RabbitMQ.")
-
+            print("Failed to connect to RabbitMQ:", e)
             if not self.fail_silently:
                 raise e
 
-    def publish_message(self, data: str, queue: str):
+    def publish_message(self, data, queue):
         channel = self.get_channel()
         channel.queue_declare(
             queue=queue, durable=True, exclusive=False, auto_delete=False
@@ -75,15 +68,11 @@ def publish_message(self, data: str, queue: str):
 
         try:
             channel.basic_publish(
-                exchange="", routing_key=queue, body=data, mandatory=True
-            )
-            logger.info(
-                "Sent message successfully.", queue_type="RabbitMQ", queue_name=queue
+                exchange="", routing_key="mongo_data", body=data, mandatory=True
             )
+            print("sent changes to RabbitMQ:", data)
         except pika.exceptions.UnroutableError:
-            logger.info(
-                "Failed to send the message.", queue_type="RabbitMQ", queue_name=queue
-            )
+            print("Message could not be confirmed")
 
     def is_connected(self) -> bool:
         return self._connection is not None and self._connection.is_open
@@ -96,5 +85,4 @@ def close(self):
         if self.is_connected():
             self._connection.close()
             self._connection = None
-
-            logger.info("Closed RabbitMQ connection.")
+            print("Closed RabbitMQ connection")
diff --git a/src/feature_pipeline/data_flow/stream_input.py b/course/module-3/data_flow/stream_input.py
similarity index 56%
rename from src/feature_pipeline/data_flow/stream_input.py
rename to course/module-3/data_flow/stream_input.py
index 65669c8..c395003 100644
--- a/src/feature_pipeline/data_flow/stream_input.py
+++ b/course/module-3/data_flow/stream_input.py
@@ -1,48 +1,36 @@
 import json
-import time
 from datetime import datetime
 from typing import Generic, Iterable, List, Optional, TypeVar
 
 from bytewax.inputs import FixedPartitionedSource, StatefulSourcePartition
-from config import settings
-from core import get_logger
-from core.mq import RabbitMQConnection
 
-logger = get_logger(__name__)
+from data_flow.mq import RabbitMQConnection
 
-DataT = TypeVar("DataT")
-MessageT = TypeVar("MessageT")
+DATA = TypeVar(
+    "DATA"
+)  # The type of the items being produced in this case the data from the queue.
+MESSAGE_ID = TypeVar(
+    "MESSAGE_ID"
+)  # The type of the state being saved and resumed in this case last message id from Rabbitmq.
 
 
-class RabbitMQPartition(StatefulSourcePartition, Generic[DataT, MessageT]):
+class RabbitMQPartition(StatefulSourcePartition, Generic[DATA, MESSAGE_ID]):
     """
     Class responsible for creating a connection between bytewax and rabbitmq that facilitates the transfer of data from mq to bytewax streaming piepline.
     Inherits StatefulSourcePartition for snapshot functionality that enables saving the state of the queue
     """
 
-    def __init__(self, queue_name: str, resume_state: MessageT | None = None) -> None:
+    def __init__(self, queue_name, resume_state=None):
         self._in_flight_msg_ids = resume_state or set()
         self.queue_name = queue_name
         self.connection = RabbitMQConnection()
         self.connection.connect()
         self.channel = self.connection.get_channel()
 
-    def next_batch(self, sched: Optional[datetime]) -> Iterable[DataT]:
-        try:
-            method_frame, header_frame, body = self.channel.basic_get(
-                queue=self.queue_name, auto_ack=True
-            )
-        except Exception:
-            logger.error(
-                f"Error while fetching message from queue.", queue_name=self.queue_name
-            )
-            time.sleep(10)  # Sleep for 10 seconds before retrying to access the queue.
-
-            self.connection.connect()
-            self.channel = self.connection.get_channel()
-
-            return []
-
+    def next_batch(self, sched: Optional[datetime]) -> Iterable[DATA]:
+        method_frame, header_frame, body = self.channel.basic_get(
+            queue=self.queue_name, auto_ack=False
+        )
         if method_frame:
             message_id = method_frame.delivery_tag
             self._in_flight_msg_ids.add(message_id)
@@ -51,7 +39,7 @@ def next_batch(self, sched: Optional[datetime]) -> Iterable[DataT]:
         else:
             return []
 
-    def snapshot(self) -> MessageT:
+    def snapshot(self) -> MESSAGE_ID:
         return self._in_flight_msg_ids
 
     def garbage_collect(self, state):
@@ -69,6 +57,6 @@ def list_parts(self) -> List[str]:
         return ["single partition"]
 
     def build_part(
-        self, now: datetime, for_part: str, resume_state: MessageT | None = None
-    ) -> StatefulSourcePartition[DataT, MessageT]:
-        return RabbitMQPartition(queue_name=settings.RABBITMQ_QUEUE_NAME)
+        self, now: datetime, for_part: str, resume_state: Optional[MESSAGE_ID]
+    ) -> StatefulSourcePartition[DATA, MESSAGE_ID]:
+        return RabbitMQPartition(queue_name="mongo_data")
diff --git a/src/feature_pipeline/data_flow/stream_output.py b/course/module-3/data_flow/stream_output.py
similarity index 52%
rename from src/feature_pipeline/data_flow/stream_output.py
rename to course/module-3/data_flow/stream_output.py
index f0d7bd6..5d2f57a 100644
--- a/src/feature_pipeline/data_flow/stream_output.py
+++ b/course/module-3/data_flow/stream_output.py
@@ -1,11 +1,9 @@
 from bytewax.outputs import DynamicSink, StatelessSinkPartition
-from core import get_logger
-from core.db.qdrant import QdrantDatabaseConnector
-from models.base import VectorDBDataModel
+from db.qdrant import QdrantDatabaseConnector
+from models.base import DBDataModel
+from qdrant_client.http.api_client import UnexpectedResponse
 from qdrant_client.models import Batch
 
-logger = get_logger(__name__)
-
 
 class QdrantOutput(DynamicSink):
     """
@@ -17,32 +15,49 @@ def __init__(self, connection: QdrantDatabaseConnector, sink_type: str):
         self._connection = connection
         self._sink_type = sink_type
 
-        collections = {
-            "cleaned_posts": False,
-            "cleaned_articles": False,
-            "cleaned_repositories": False,
-            "vector_posts": True,
-            "vector_articles": True,
-            "vector_repositories": True,
-        }
-
-        for collection_name, is_vector in collections.items():
-            try:
-                self._connection.get_collection(collection_name=collection_name)
-            except Exception:
-                logger.warning(
-                    "Couldn't access the collection. Creating a new one...",
-                    collection_name=collection_name,
-                )
-
-                if is_vector:
-                    self._connection.create_vector_collection(
-                        collection_name=collection_name
-                    )
-                else:
-                    self._connection.create_non_vector_collection(
-                        collection_name=collection_name
-                    )
+        try:
+            self._connection.get_collection(collection_name="cleaned_posts")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_non_vector_collection(
+                collection_name="cleaned_posts"
+            )
+
+        try:
+            self._connection.get_collection(collection_name="cleaned_articles")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_non_vector_collection(
+                collection_name="cleaned_articles"
+            )
+
+        try:
+            self._connection.get_collection(collection_name="cleaned_repositories")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_non_vector_collection(
+                collection_name="cleaned_repositories"
+            )
+
+        try:
+            self._connection.get_collection(collection_name="vector_posts")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_vector_collection(collection_name="vector_posts")
+
+        try:
+            self._connection.get_collection(collection_name="vector_articles")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_vector_collection(collection_name="vector_articles")
+
+        try:
+            self._connection.get_collection(collection_name="vector_repositories")
+        except UnexpectedResponse as e:
+            print(f"Error when accessing the collection: {e}")
+            self._connection.create_vector_collection(
+                collection_name="vector_repositories"
+            )
 
     def build(self, worker_index: int, worker_count: int) -> StatelessSinkPartition:
         if self._sink_type == "clean":
@@ -57,8 +72,8 @@ class QdrantCleanedDataSink(StatelessSinkPartition):
     def __init__(self, connection: QdrantDatabaseConnector):
         self._client = connection
 
-    def write_batch(self, items: list[VectorDBDataModel]) -> None:
-        payloads = [item.to_payload() for item in items]
+    def write_batch(self, items: list[DBDataModel]) -> None:
+        payloads = [item.save() for item in items]
         ids, data = zip(*payloads)
         collection_name = get_clean_collection(data_type=data[0]["type"])
         self._client.write_data(
@@ -66,19 +81,13 @@ def write_batch(self, items: list[VectorDBDataModel]) -> None:
             points=Batch(ids=ids, vectors={}, payloads=data),
         )
 
-        logger.info(
-            "Successfully inserted requested cleaned point(s)",
-            collection_name=collection_name,
-            num=len(ids),
-        )
-
 
 class QdrantVectorDataSink(StatelessSinkPartition):
     def __init__(self, connection: QdrantDatabaseConnector):
         self._client = connection
 
-    def write_batch(self, items: list[VectorDBDataModel]) -> None:
-        payloads = [item.to_payload() for item in items]
+    def write_batch(self, items: list[DBDataModel]) -> None:
+        payloads = [item.save() for item in items]
         ids, vectors, meta_data = zip(*payloads)
         collection_name = get_vector_collection(data_type=meta_data[0]["type"])
         self._client.write_data(
@@ -86,12 +95,6 @@ def write_batch(self, items: list[VectorDBDataModel]) -> None:
             points=Batch(ids=ids, vectors=vectors, payloads=meta_data),
         )
 
-        logger.info(
-            "Successfully inserted requested vector point(s)",
-            collection_name=collection_name,
-            num=len(ids),
-        )
-
 
 def get_clean_collection(data_type: str) -> str:
     if data_type == "posts":
diff --git a/src/bonus_superlinked_rag/data_logic/__init__.py b/course/module-3/data_logic/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/data_logic/__init__.py
rename to course/module-3/data_logic/__init__.py
diff --git a/src/feature_pipeline/data_logic/chunking_data_handlers.py b/course/module-3/data_logic/chunking_data_handlers.py
similarity index 100%
rename from src/feature_pipeline/data_logic/chunking_data_handlers.py
rename to course/module-3/data_logic/chunking_data_handlers.py
diff --git a/src/feature_pipeline/data_logic/cleaning_data_handlers.py b/course/module-3/data_logic/cleaning_data_handlers.py
similarity index 75%
rename from src/feature_pipeline/data_logic/cleaning_data_handlers.py
rename to course/module-3/data_logic/cleaning_data_handlers.py
index f79dafa..87ca54a 100644
--- a/src/feature_pipeline/data_logic/cleaning_data_handlers.py
+++ b/course/module-3/data_logic/cleaning_data_handlers.py
@@ -19,14 +19,10 @@ def clean(self, data_model: DataModel) -> DataModel:
 
 class PostCleaningHandler(CleaningDataHandler):
     def clean(self, data_model: PostsRawModel) -> PostCleanedModel:
-        joined_text = (
-            "".join(data_model.content.values()) if data_model and data_model.content else None
-        )
-
         return PostCleanedModel(
             entry_id=data_model.entry_id,
             platform=data_model.platform,
-            cleaned_content=clean_text(joined_text),
+            cleaned_content=clean_text("".join(data_model.content.values())),
             author_id=data_model.author_id,
             image=data_model.image if data_model.image else None,
             type=data_model.type,
@@ -35,15 +31,11 @@ def clean(self, data_model: PostsRawModel) -> PostCleanedModel:
 
 class ArticleCleaningHandler(CleaningDataHandler):
     def clean(self, data_model: ArticleRawModel) -> ArticleCleanedModel:
-        joined_text = (
-            "".join(data_model.content.values()) if data_model and data_model.content else None
-        )
-
         return ArticleCleanedModel(
             entry_id=data_model.entry_id,
             platform=data_model.platform,
             link=data_model.link,
-            cleaned_content=clean_text(joined_text),
+            cleaned_content=clean_text("".join(data_model.content.values())),
             author_id=data_model.author_id,
             type=data_model.type,
         )
@@ -51,15 +43,11 @@ def clean(self, data_model: ArticleRawModel) -> ArticleCleanedModel:
 
 class RepositoryCleaningHandler(CleaningDataHandler):
     def clean(self, data_model: RepositoryRawModel) -> RepositoryCleanedModel:
-        joined_text = (
-            "".join(data_model.content.values()) if data_model and data_model.content else None
-        )
-
         return RepositoryCleanedModel(
             entry_id=data_model.entry_id,
             name=data_model.name,
             link=data_model.link,
-            cleaned_content=clean_text(joined_text),
+            cleaned_content=clean_text("".join(data_model.content.values())),
             owner_id=data_model.owner_id,
             type=data_model.type,
         )
diff --git a/src/feature_pipeline/data_logic/dispatchers.py b/course/module-3/data_logic/dispatchers.py
similarity index 84%
rename from src/feature_pipeline/data_logic/dispatchers.py
rename to course/module-3/data_logic/dispatchers.py
index 0f162a6..0833ed2 100644
--- a/src/feature_pipeline/data_logic/dispatchers.py
+++ b/course/module-3/data_logic/dispatchers.py
@@ -1,7 +1,3 @@
-from core import get_logger
-from models.base import DataModel
-from models.raw import ArticleRawModel, PostsRawModel, RepositoryRawModel
-
 from data_logic.chunking_data_handlers import (
     ArticleChunkingHandler,
     ChunkingDataHandler,
@@ -20,17 +16,14 @@
     PostEmbeddingHandler,
     RepositoryEmbeddingHandler,
 )
-
-logger = get_logger(__name__)
+from models.base import DataModel
+from models.raw import ArticleRawModel, PostsRawModel, RepositoryRawModel
 
 
 class RawDispatcher:
     @staticmethod
     def handle_mq_message(message: dict) -> DataModel:
         data_type = message.get("type")
-
-        logger.info("Received message.", data_type=data_type)
-
         if data_type == "posts":
             return PostsRawModel(**message)
         elif data_type == "articles":
@@ -62,13 +55,6 @@ def dispatch_cleaner(cls, data_model: DataModel) -> DataModel:
         data_type = data_model.type
         handler = cls.cleaning_factory.create_handler(data_type)
         clean_model = handler.clean(data_model)
-
-        logger.info(
-            "Data cleaned successfully.",
-            data_type=data_type,
-            cleaned_content_len=len(clean_model.cleaned_content),
-        )
-
         return clean_model
 
 
@@ -93,13 +79,6 @@ def dispatch_chunker(cls, data_model: DataModel) -> list[DataModel]:
         data_type = data_model.type
         handler = cls.cleaning_factory.create_handler(data_type)
         chunk_models = handler.chunk(data_model)
-
-        logger.info(
-            "Cleaned content chunked successfully.",
-            num=len(chunk_models),
-            data_type=data_type,
-        )
-
         return chunk_models
 
 
@@ -124,11 +103,4 @@ def dispatch_embedder(cls, data_model: DataModel) -> DataModel:
         data_type = data_model.type
         handler = cls.cleaning_factory.create_handler(data_type)
         embedded_chunk_model = handler.embedd(data_model)
-
-        logger.info(
-            "Chunk embedded successfully.",
-            data_type=data_type,
-            embedding_len=len(embedded_chunk_model.embedded_content),
-        )
-
         return embedded_chunk_model
diff --git a/src/feature_pipeline/data_logic/embedding_data_handlers.py b/course/module-3/data_logic/embedding_data_handlers.py
similarity index 100%
rename from src/feature_pipeline/data_logic/embedding_data_handlers.py
rename to course/module-3/data_logic/embedding_data_handlers.py
diff --git a/src/bonus_superlinked_rag/llm/__init__.py b/course/module-3/db/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/llm/__init__.py
rename to course/module-3/db/__init__.py
diff --git a/src/core/db/documents.py b/course/module-3/db/documents.py
similarity index 76%
rename from src/core/db/documents.py
rename to course/module-3/db/documents.py
index 24868a8..abf3c92 100644
--- a/src/core/db/documents.py
+++ b/course/module-3/db/documents.py
@@ -4,13 +4,10 @@
 from pydantic import UUID4, BaseModel, ConfigDict, Field
 from pymongo import errors
 
-import core.logger_utils as logger_utils
-from core.db.mongo import connection
-from core.errors import ImproperlyConfigured
+from db.errors import ImproperlyConfigured
+from db.mongo import connection
 
-_database = connection.get_database("twin")
-
-logger = logger_utils.get_logger(__name__)
+_database = connection.get_database("scrabble")
 
 
 class BaseDocument(BaseModel):
@@ -47,9 +44,8 @@ def save(self, **kwargs):
         try:
             result = collection.insert_one(self.to_mongo(**kwargs))
             return result.inserted_id
-        except errors.WriteError:
-            logger.exception("Failed to insert document.")
-
+        except errors.WriteError as e:
+            print(f"Failed to insert document {e}")
             return None
 
     @classmethod
@@ -62,23 +58,8 @@ def get_or_create(cls, **filter_options) -> Optional[str]:
             new_instance = cls(**filter_options)
             new_instance = new_instance.save()
             return new_instance
-        except errors.OperationFailure:
-            logger.exception("Failed to retrieve or create document.")
-
-            return None
-
-    @classmethod
-    def find(cls, **filter_options):
-        collection = _database[cls._get_collection_name()]
-        try:
-            instance = collection.find_one(filter_options)
-            if instance:
-                return cls.from_mongo(instance)
-
-            return None
-        except errors.OperationFailure:
-            logger.error("Failed to retrieve document")
-
+        except errors.OperationFailure as e:
+            print(f"Failed to retrieve document: {e}")
             return None
 
     @classmethod
@@ -89,9 +70,8 @@ def bulk_insert(cls, documents: List, **kwargs) -> Optional[List[str]]:
                 [doc.to_mongo(**kwargs) for doc in documents]
             )
             return result.inserted_ids
-        except errors.WriteError:
-            logger.exception("Failed to insert documents.")
-
+        except errors.WriteError as e:
+            print(f"Failed to insert document {e}")
             return None
 
     @classmethod
diff --git a/course/module-3/db/errors.py b/course/module-3/db/errors.py
new file mode 100644
index 0000000..5478443
--- /dev/null
+++ b/course/module-3/db/errors.py
@@ -0,0 +1,6 @@
+class ScrabbleException(Exception):
+    pass
+
+
+class ImproperlyConfigured(ScrabbleException):
+    pass
diff --git a/course/module-3/db/mongo.py b/course/module-3/db/mongo.py
new file mode 100644
index 0000000..67cd5f6
--- /dev/null
+++ b/course/module-3/db/mongo.py
@@ -0,0 +1,32 @@
+from pymongo import MongoClient
+from pymongo.errors import ConnectionFailure
+
+from settings import settings
+
+
+class MongoDatabaseConnector:
+    _instance: MongoClient = None
+
+    def __new__(cls, *args, **kwargs):
+        if cls._instance is None:
+            try:
+                cls._instance = MongoClient(settings.MONGO_DATABASE_HOST)
+            except ConnectionFailure as e:
+                print(f"Couldn't connect to the database: {str(e)}")
+                raise
+
+        print(
+            f"Connection to database with uri: {settings.MONGO_DATABASE_HOST} successful"
+        )
+        return cls._instance
+
+    def get_database(self):
+        return self._instance[settings.MONGO_DATABASE_NAME]
+
+    def close(self):
+        if self._instance:
+            self._instance.close()
+            print("Connected to database has been closed.")
+
+
+connection = MongoDatabaseConnector()
diff --git a/src/core/db/qdrant.py b/course/module-3/db/qdrant.py
similarity index 50%
rename from src/core/db/qdrant.py
rename to course/module-3/db/qdrant.py
index 6fc2267..7c7c881 100644
--- a/src/core/db/qdrant.py
+++ b/course/module-3/db/qdrant.py
@@ -1,32 +1,28 @@
-from qdrant_client import QdrantClient, models
+from qdrant_client import QdrantClient
+from qdrant_client.http.exceptions import UnexpectedResponse
 from qdrant_client.http.models import Batch, Distance, VectorParams
-
-import core.logger_utils as logger_utils
-from core.config import settings
-
-logger = logger_utils.get_logger(__name__)
+from settings import settings
 
 
 class QdrantDatabaseConnector:
-    _instance: QdrantClient | None = None
+    _instance: QdrantClient = None
 
-    def __init__(self) -> None:
+    def __init__(self):
         if self._instance is None:
-            if settings.USE_QDRANT_CLOUD:
-                self._instance = QdrantClient(
-                    url=settings.QDRANT_CLOUD_URL,
-                    api_key=settings.QDRANT_APIKEY,
-                )
-            else:
+            try:
                 self._instance = QdrantClient(
                     host=settings.QDRANT_DATABASE_HOST,
                     port=settings.QDRANT_DATABASE_PORT,
                 )
+            except UnexpectedResponse as e:
+                print(f"Couldn't connect to the database: {str(e)}")
+                raise
 
     def get_collection(self, collection_name: str):
         return self._instance.get_collection(collection_name=collection_name)
 
     def create_non_vector_collection(self, collection_name: str):
+        print(f"Created collection: {collection_name}")
         self._instance.create_collection(
             collection_name=collection_name, vectors_config={}
         )
@@ -42,30 +38,17 @@ def create_vector_collection(self, collection_name: str):
     def write_data(self, collection_name: str, points: Batch):
         try:
             self._instance.upsert(collection_name=collection_name, points=points)
-        except Exception:
-            logger.exception("An error occurred while inserting data.")
-
+            print(
+                f"Successfully inserted {len(points.ids)} point(s) into {collection_name}."
+            )
+        except Exception as e:
+            print(f"An error occurred while inserting data: {str(e)}")
             raise
 
-    def search(
-        self,
-        collection_name: str,
-        query_vector: list,
-        query_filter: models.Filter | None = None,
-        limit: int = 3,
-    ) -> list:
-        return self._instance.search(
-            collection_name=collection_name,
-            query_vector=query_vector,
-            query_filter=query_filter,
-            limit=limit,
-        )
-
-    def scroll(self, collection_name: str, limit: int):
-        return self._instance.scroll(collection_name=collection_name, limit=limit)
-
     def close(self):
         if self._instance:
             self._instance.close()
+            print("Connected to database has been closed.")
+
 
-            logger.info("Connected to database has been closed.")
+connection = QdrantDatabaseConnector()
diff --git a/course/module-3/deploy/Dockerfile b/course/module-3/deploy/Dockerfile
new file mode 100644
index 0000000..4911e8a
--- /dev/null
+++ b/course/module-3/deploy/Dockerfile
@@ -0,0 +1,38 @@
+FROM python:3.11-slim-bullseye
+
+# Set environment variables
+ENV WORKSPACE_ROOT=/usr/src/app \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    POETRY_HOME="/opt/poetry" \
+    POETRY_NO_INTERACTION=1
+
+RUN mkdir -p $WORKSPACE_ROOT
+
+RUN apt-get update -y \
+    && apt-get install -y --no-install-recommends build-essential \
+    curl \
+    libssl-dev \
+    && apt-get clean
+
+# Install Poetry - respects $POETRY_VERSION & $POETRY_HOME
+RUN curl -sSL https://install.python-poetry.org | python -
+ENV PATH="$POETRY_HOME/bin:$PATH"
+
+RUN apt-get remove -y curl
+
+COPY poetry.lock pyproject.toml ./
+
+RUN poetry config virtualenvs.create false  \
+    && poetry install \
+    && rm poetry.lock pyproject.toml
+
+WORKDIR $WORKSPACE_ROOT
+
+COPY . .
+
+# Give execution permission to your shell script
+RUN chmod +x ./deploy/entrypoint.sh
+
+# Run your shell script
+CMD ["./deploy/entrypoint.sh"]
diff --git a/src/bonus_superlinked_rag/scripts/bytewax_entrypoint.sh b/course/module-3/deploy/entrypoint.sh
similarity index 69%
rename from src/bonus_superlinked_rag/scripts/bytewax_entrypoint.sh
rename to course/module-3/deploy/entrypoint.sh
index 545db5f..c7ad37f 100644
--- a/src/bonus_superlinked_rag/scripts/bytewax_entrypoint.sh
+++ b/course/module-3/deploy/entrypoint.sh
@@ -2,17 +2,16 @@
 
 if [ "$DEBUG" = true ]
 then
-    python -m bytewax.run "tools.run_real_time:build_flow(debug=True)"
+    poetry run python -m bytewax.run "tools.run_real_time:build_flow(debug=True)"
 else
     if [ "$BYTEWAX_PYTHON_FILE_PATH" = "" ]
     then
         echo 'BYTEWAX_PYTHON_FILE_PATH is not set. Exiting...'
         exit 1
     fi
-    python -m bytewax.run $BYTEWAX_PYTHON_FILE_PATH
+    poetry run python -m bytewax.run $BYTEWAX_PYTHON_FILE_PATH
 fi
 
-
 echo 'Process ended.'
 
 if [ "$BYTEWAX_KEEP_CONTAINER_ALIVE" = true ]
diff --git a/course/module-3/docker-compose.yml b/course/module-3/docker-compose.yml
new file mode 100644
index 0000000..e6e709d
--- /dev/null
+++ b/course/module-3/docker-compose.yml
@@ -0,0 +1,69 @@
+version: '3.8'
+
+services:
+  mongo1:
+    image: mongo:5
+    container_name: mongo1
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30001"]
+    volumes:
+      - ./data/mongo-1:/data/db
+    ports:
+      - 30001:30001
+    healthcheck:
+      test: test $$(echo "rs.initiate({_id:'my-replica-set',members:[{_id:0,host:\"mongo1:30001\"},{_id:1,host:\"mongo2:30002\"},{_id:2,host:\"mongo3:30003\"}]}).ok || rs.status().ok" | mongo --port 30001 --quiet) -eq 1
+      interval: 10s
+      start_period: 30s
+
+  mongo2:
+    image: mongo:5
+    container_name: mongo2
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30002"]
+    volumes:
+      - ./data/mongo-2:/data/db
+    ports:
+      - 30002:30002
+
+  mongo3:
+    image: mongo:5
+    container_name: mongo3
+    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30003"]
+    volumes:
+      - ./data/mongo-3:/data/db
+    ports:
+      - 30003:30003
+
+  mq:
+    image: rabbitmq:3-management-alpine
+    container_name: scrabble_mq
+    ports:
+      - "5673:5672"
+      - "15673:15672"
+    volumes:
+      - ~/rabbitmq/data/:/var/lib/rabbitmq/
+      - ~/rabbitmq/log/:/var/log/rabbitmq
+    restart: always
+
+  qdrant:
+    image: qdrant/qdrant:latest
+    container_name: scrabble_qdrant
+    ports:
+      - 6333:6333
+      - 6334:6334
+    expose:
+      - 6333
+      - 6334
+      - 6335
+    # configs:
+    #   - source: qdrant_config
+    #     target: /qdrant/config/production.yaml
+    volumes:
+      - qdrant_data:/qdrant_data
+    restart: always
+
+# configs:
+#   qdrant_config:
+#     content: |
+#       log_level: INFO
+
+volumes:
+  qdrant_data:
\ No newline at end of file
diff --git a/course/module-3/insert_data_mongo.py b/course/module-3/insert_data_mongo.py
new file mode 100644
index 0000000..c81a61e
--- /dev/null
+++ b/course/module-3/insert_data_mongo.py
@@ -0,0 +1,177 @@
+from db.documents import ArticleDocument, PostDocument, RepositoryDocument
+
+
+def insert_post_into_collection_1():
+    post = {
+        "first post": """Excited to share some big news! 🎉 Our team has been working tirelessly on developing a groundbreaking solution designed to revolutionize the tech industry, and today, we're finally ready to unveil it to the world. Introducing InnovateX, our latest product that leverages cutting-edge AI to streamline operations and enhance productivity for businesses globally. 🚀
+
+    InnovateX is not just a product; it's a game-changer. It embodies our commitment to innovation, efficiency, and excellence. By harnessing the power of advanced algorithms and machine learning, InnovateX offers unparalleled insights and automation capabilities that transform the way businesses operate. 🌟
+
+    But what sets InnovateX apart? It's the meticulous attention to detail, the user-centric design, and the relentless pursuit of perfection. Our team has poured their hearts and souls into creating a product that not only meets but exceeds the expectations of our users. From intuitive interfaces to robust security features, InnovateX is built to empower. 💪
+
+    As we embark on this exciting journey, I want to extend my heartfelt thanks to every team member who made this possible. Your dedication, creativity, and passion are what drive our success. To our clients and partners, thank you for believing in us and joining us on this path to innovation. We're thrilled to see how InnovateX will help you achieve your goals and redefine what's possible. 🌈
+
+    Looking ahead, we're committed to continuous improvement and innovation. We believe that the best is yet to come, and with InnovateX, we're just getting started. Stay tuned for more updates, and here's to a future filled with endless possibilities! 🌍
+
+    #Innovation #TechRevolution #InnovateX #TeamSuccess #ThankYou"""
+    }
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_2():
+    post = {
+        'first post': """Join me in acknowledging the transformative impact of OpenAI's groundbreaking work! From revolutionizing natural language processing to advancing robotics and healthcare, OpenAI's research has far-reaching implications across diverse fields. By fostering collaboration and knowledge sharing, OpenAI is accelerating the pace of innovation and driving progress in AI-driven technologies. As we witness the remarkable achievements of OpenAI, let's continue to support and amplify their efforts in shaping a future where AI serves as a powerful force for innovation and societal benefit. #OpenAI #AI #Innovation #Technology"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_3():
+    post = {
+        'first post': """Excited to delve deeper into the fascinating realm of Retrieval Augmented Generation (RAG)! This cutting-edge approach combines the strengths of retrieval-based models with generative models, promising groundbreaking advancements in natural language processing (NLP). By enabling systems to retrieve and generate information dynamically, RAG holds immense potential for revolutionizing various applications, from question answering systems to content creation tools. I'm eager to explore how RAG can enhance user experiences, streamline information retrieval processes, and drive innovation across industries. Join me on this journey as we uncover the transformative capabilities of RAG in shaping the future of AI-driven technologies. #RAG #AI #NLP #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_4():
+    post = {
+        'first post': """Just wrapped up an intensive exploration of Retrieval Augmented Generation (RAG), and I'm truly astounded by its potential to redefine the landscape of artificial intelligence! By seamlessly integrating retrieval and generation models, RAG introduces a novel paradigm that empowers AI systems to dynamically retrieve and generate information in real-time. This innovative approach opens up new avenues for enhancing human-computer interactions, enabling more intuitive and contextually relevant responses. From personalized virtual assistants to AI-driven content creation platforms, the applications of RAG are boundless. I'm excited to witness the transformative impact of RAG across various domains and industries. #ArtificialIntelligence #RAG #Innovation #FutureTech"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_5():
+    post = {
+        'first post': """Are you intrigued by the convergence of retrieval and generation models in natural language processing? Join me on a journey of discovery as we explore the fascinating world of Retrieval Augmented Generation (RAG). By seamlessly integrating the strengths of both approaches, RAG offers a powerful framework for enhancing information retrieval, question answering, and content generation tasks. Its ability to dynamically retrieve and generate contextually relevant responses marks a significant leap forward in AI capabilities. Let's embark on this exploration together and unlock the full potential of RAG in shaping the future of AI-driven technologies. #RAG #AI #NLP #Technology"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe4200-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_6():
+    post = {
+        'first post': """Just completed an in-depth study of Retrieval Augmented Generation (RAG), and I'm incredibly excited about its transformative potential in natural language processing (NLP). By leveraging the complementary strengths of retrieval-based and generative models, RAG enables AI systems to dynamically retrieve and generate information, leading to more contextually relevant and coherent responses. Whether it's enhancing search engines, improving chatbots, or revolutionizing content creation tools, RAG offers a versatile framework with countless applications. I can't wait to see how this innovative approach will reshape the way we interact with information and AI systems in the years to come. #RAG #NLP #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_7():
+    post = {
+        'first post': """Just completed an in-depth study of Retrieval Augmented Generation (RAG), and I'm incredibly excited about its transformative potential in natural language processing (NLP). By leveraging the complementary strengths of retrieval-based and generative models, RAG enables AI systems to dynamically retrieve and generate information, leading to more contextually relevant and coherent responses. Whether it's enhancing search engines, improving chatbots, or revolutionizing content creation tools, RAG offers a versatile framework with countless applications. I can't wait to see how this innovative approach will reshape the way we interact with information and AI systems in the years to come. #RAG #NLP #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_8():
+    post = {
+        'first post': """Just attended an enlightening webinar on Retrieval Augmented Generation (RAG) and its potential to revolutionize content creation in the digital age. By seamlessly integrating retrieval-based and generative models, RAG offers a unique solution to the challenges of generating high-quality and contextually relevant content. Its ability to dynamically retrieve information from large knowledge bases while generating coherent and engaging narratives opens up exciting possibilities for marketers, writers, and content creators. Whether it's crafting personalized product descriptions or generating compelling blog posts, RAG promises to elevate the art of content creation to new heights. #RAG #ContentCreation #AI #Innovation"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_9():
+    post = {
+        'first post': """Python classes are a fundamental concept in object-oriented programming, and mastering them is essential for building robust and scalable applications. Whether you're a beginner eager to learn the basics or an experienced developer looking to deepen your understanding, delving into Python classes is a rewarding journey. From encapsulation and inheritance to polymorphism and abstraction, classes empower you to organize your code effectively and create reusable components. Join me in exploring the intricacies of Python classes as we unlock the full potential of object-oriented programming in Python. Let's level up our coding skills together! #Python #Programming #Classes #OOP"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_post_into_collection_10():
+    post = {
+        'first post': """Let's dive into the world of Python classes! As the building blocks of object-oriented programming in Python, classes play a pivotal role in structuring and organizing code for scalability and maintainability. Whether you're developing web applications, data analysis tools, or machine learning models, understanding how to leverage classes effectively is essential. From defining attributes and methods to implementing inheritance and polymorphism, mastering Python classes empowers you to create elegant and efficient solutions to complex problems. Join me in exploring the depths of Python classes as we elevate our coding skills to new heights! #Python #OOP #Coding #Programming"""}
+    PostDocument(
+        platform="linkedin",
+        content=post,
+        author_id="dbe4200-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+    print("Post inserted into collection")
+
+
+def insert_article_into_collection_1():
+    content = {"test": "Test data for a article content"}
+    ArticleDocument(
+        platform="medium",
+        link="/htttps/alex/paul",
+        content=content,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+
+    print("Article inserted into collection")
+
+
+def insert_article_into_collection_2():
+    content = {"test": """Retrieval Augmented Generation (RAG) represents a cutting-edge approach in the field of natural language processing (NLP) that merges the strengths of retrieval-based models and generative models. The key concept behind RAG is its ability to dynamically retrieve relevant information from a large corpus or knowledge base and seamlessly incorporate it into generative responses.
+    In traditional generative models, outputs are generated based solely on the internal knowledge of the model. This can lead to issues with outdated information or hallucinations, where the model creates content that seems plausible but is factually incorrect. RAG mitigates these risks by integrating retrieval components that can access up-to-date and contextually appropriate information in real-time. This hybrid approach enhances accuracy and contextual relevance, making it ideal for applications like question answering, chatbots, and content creation tools.
+    RAG's impact is transformative, enabling AI systems to provide more reliable and accurate responses while adapting to changing information landscapes. This flexibility opens the door to a range of new possibilities, from personalized virtual assistants to intelligent document summarization. As RAG continues to evolve, it holds great promise for revolutionizing how we interact with AI systems and extract valuable insights from large volumes of data."""}
+    ArticleDocument(
+        platform="medium",
+        link="/htttps/alex/paul",
+        content=content,
+        author_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+
+    print("Article inserted into collection")
+
+
+def insert_repo_into_collection():
+    content = {"test": "Test data for a repo content into db"}
+    RepositoryDocument(
+        name="test_repo",
+        link="/bla/bla/bla",
+        content=content,
+        owner_id="dbe92510-c33f-4ff7-9908-ee6356fe251f",
+    ).save()
+
+    print("Repository inserted into collection")
+
+
+if __name__ == "__main__":
+    insert_post_into_collection_1()
+    insert_post_into_collection_2()
+    insert_post_into_collection_3()
+    insert_post_into_collection_4()
+    insert_post_into_collection_5()
+    insert_post_into_collection_6()
+    insert_post_into_collection_7()
+    insert_post_into_collection_8()
+    insert_post_into_collection_9()
+    insert_post_into_collection_10()
+    insert_article_into_collection_1()
+    insert_article_into_collection_2()
+    insert_repo_into_collection()
diff --git a/src/bonus_superlinked_rag/rag/__init__.py b/course/module-3/llm_components/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/rag/__init__.py
rename to course/module-3/llm_components/__init__.py
diff --git a/src/bonus_superlinked_rag/llm/chain.py b/course/module-3/llm_components/chain.py
similarity index 100%
rename from src/bonus_superlinked_rag/llm/chain.py
rename to course/module-3/llm_components/chain.py
diff --git a/course/module-3/llm_components/prompt_templates.py b/course/module-3/llm_components/prompt_templates.py
new file mode 100644
index 0000000..534634c
--- /dev/null
+++ b/course/module-3/llm_components/prompt_templates.py
@@ -0,0 +1,64 @@
+from abc import ABC, abstractmethod
+
+from langchain.prompts import PromptTemplate
+from pydantic import BaseModel
+
+
+class BasePromptTemplate(ABC, BaseModel):
+    @abstractmethod
+    def create_template(self) -> PromptTemplate:
+        pass
+
+
+class QueryExpansionTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to generate Five
+    different versions of the given user question to retrieve relevant documents from a vector
+    database. By generating multiple perspectives on the user question, your goal is to help
+    the user overcome some of the limitations of the distance-based similarity search.
+    Provide these alternative questions seperated by newlines.
+    Original question: {question}"""
+
+    question: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question"], verbose=True
+        )
+        template.format(question=self.question)
+        return template
+
+
+class SelfQueryTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to extract information from a user question.
+    The required information that needs to be extracted is the user id. 
+    Your response should consists of only the extracted id (e.g. 1345256), nothing else.
+    User question: {question}"""
+
+    question: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question"], verbose=True
+        )
+        template.format(question=self.question)
+        return template
+
+
+class RerankingTemplate(BasePromptTemplate):
+    prompt: str = """You are an AI language model assistant. Your task is to rerank passages related to a query
+    based on their relevance. The most relevant passages should be put at the beginning and at the end. 
+    You should only pick at max 5 passages.
+    The following are passages related to this query: {question}.
+    Passages: {passages}
+    """
+
+    question: str
+
+    passages: str
+
+    def create_template(self) -> PromptTemplate:
+        template = PromptTemplate(
+            template=self.prompt, input_variables=["question", "passages"], verbose=True
+        )
+        template.format(question=self.question, passages=self.passages)
+        return template
diff --git a/src/bonus_superlinked_rag/server/runner/executor/__init__.py b/course/module-3/models/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/server/runner/executor/__init__.py
rename to course/module-3/models/__init__.py
diff --git a/src/feature_pipeline/models/base.py b/course/module-3/models/base.py
similarity index 63%
rename from src/feature_pipeline/models/base.py
rename to course/module-3/models/base.py
index 52b28d2..f4c5b39 100644
--- a/src/feature_pipeline/models/base.py
+++ b/course/module-3/models/base.py
@@ -3,16 +3,16 @@
 from pydantic import BaseModel
 
 
-class DataModel(BaseModel):
+class DataModel(ABC, BaseModel):
     """
-    Abstract class for all data models
+    Abstract class for all data model
     """
 
-    entry_id: str
+    entry_id: int
     type: str
 
 
-class VectorDBDataModel(ABC, DataModel):
+class DBDataModel(DataModel):
     """
     Abstract class for all data models that need to be saved into a vector DB (e.g. Qdrant)
     """
@@ -21,5 +21,5 @@ class VectorDBDataModel(ABC, DataModel):
     type: str
 
     @abstractmethod
-    def to_payload(self) -> tuple:
+    def save(self) -> tuple:
         pass
diff --git a/src/feature_pipeline/models/chunk.py b/course/module-3/models/chunk.py
similarity index 100%
rename from src/feature_pipeline/models/chunk.py
rename to course/module-3/models/chunk.py
diff --git a/src/feature_pipeline/models/clean.py b/course/module-3/models/clean.py
similarity index 78%
rename from src/feature_pipeline/models/clean.py
rename to course/module-3/models/clean.py
index e736937..f4eea27 100644
--- a/src/feature_pipeline/models/clean.py
+++ b/course/module-3/models/clean.py
@@ -1,9 +1,9 @@
 from typing import Optional, Tuple
 
-from models.base import VectorDBDataModel
+from models.base import DBDataModel
 
 
-class PostCleanedModel(VectorDBDataModel):
+class PostCleanedModel(DBDataModel):
     entry_id: str
     platform: str
     cleaned_content: str
@@ -11,7 +11,7 @@ class PostCleanedModel(VectorDBDataModel):
     image: Optional[str] = None
     type: str
 
-    def to_payload(self) -> Tuple[str, dict]:
+    def save(self) -> Tuple[str, dict]:
         data = {
             "platform": self.platform,
             "author_id": self.author_id,
@@ -23,7 +23,7 @@ def to_payload(self) -> Tuple[str, dict]:
         return self.entry_id, data
 
 
-class ArticleCleanedModel(VectorDBDataModel):
+class ArticleCleanedModel(DBDataModel):
     entry_id: str
     platform: str
     link: str
@@ -31,7 +31,7 @@ class ArticleCleanedModel(VectorDBDataModel):
     author_id: str
     type: str
 
-    def to_payload(self) -> Tuple[str, dict]:
+    def save(self) -> Tuple[str, dict]:
         data = {
             "platform": self.platform,
             "link": self.link,
@@ -43,7 +43,7 @@ def to_payload(self) -> Tuple[str, dict]:
         return self.entry_id, data
 
 
-class RepositoryCleanedModel(VectorDBDataModel):
+class RepositoryCleanedModel(DBDataModel):
     entry_id: str
     name: str
     link: str
@@ -51,7 +51,7 @@ class RepositoryCleanedModel(VectorDBDataModel):
     owner_id: str
     type: str
 
-    def to_payload(self) -> Tuple[str, dict]:
+    def save(self) -> Tuple[str, dict]:
         data = {
             "name": self.name,
             "link": self.link,
diff --git a/src/feature_pipeline/models/embedded_chunk.py b/course/module-3/models/embedded_chunk.py
similarity index 80%
rename from src/feature_pipeline/models/embedded_chunk.py
rename to course/module-3/models/embedded_chunk.py
index 23ca8a2..94e5f9c 100644
--- a/src/feature_pipeline/models/embedded_chunk.py
+++ b/course/module-3/models/embedded_chunk.py
@@ -2,10 +2,10 @@
 
 import numpy as np
 
-from models.base import VectorDBDataModel
+from models.base import DBDataModel
 
 
-class PostEmbeddedChunkModel(VectorDBDataModel):
+class PostEmbeddedChunkModel(DBDataModel):
     entry_id: str
     platform: str
     chunk_id: str
@@ -17,7 +17,7 @@ class PostEmbeddedChunkModel(VectorDBDataModel):
     class Config:
         arbitrary_types_allowed = True
 
-    def to_payload(self) -> Tuple[str, np.ndarray, dict]:
+    def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "platform": self.platform,
@@ -29,7 +29,7 @@ def to_payload(self) -> Tuple[str, np.ndarray, dict]:
         return self.chunk_id, self.embedded_content, data
 
 
-class ArticleEmbeddedChunkModel(VectorDBDataModel):
+class ArticleEmbeddedChunkModel(DBDataModel):
     entry_id: str
     platform: str
     link: str
@@ -42,7 +42,7 @@ class ArticleEmbeddedChunkModel(VectorDBDataModel):
     class Config:
         arbitrary_types_allowed = True
 
-    def to_payload(self) -> Tuple[str, np.ndarray, dict]:
+    def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "platform": self.platform,
@@ -55,7 +55,7 @@ def to_payload(self) -> Tuple[str, np.ndarray, dict]:
         return self.chunk_id, self.embedded_content, data
 
 
-class RepositoryEmbeddedChunkModel(VectorDBDataModel):
+class RepositoryEmbeddedChunkModel(DBDataModel):
     entry_id: str
     name: str
     link: str
@@ -68,7 +68,7 @@ class RepositoryEmbeddedChunkModel(VectorDBDataModel):
     class Config:
         arbitrary_types_allowed = True
 
-    def to_payload(self) -> Tuple[str, np.ndarray, dict]:
+    def save(self) -> Tuple[str, np.ndarray, dict]:
         data = {
             "id": self.entry_id,
             "name": self.name,
diff --git a/src/feature_pipeline/models/raw.py b/course/module-3/models/raw.py
similarity index 76%
rename from src/feature_pipeline/models/raw.py
rename to course/module-3/models/raw.py
index c484090..8fd8dde 100644
--- a/src/feature_pipeline/models/raw.py
+++ b/course/module-3/models/raw.py
@@ -4,21 +4,27 @@
 
 
 class RepositoryRawModel(DataModel):
+    entry_id: str
     name: str
     link: str
     content: dict
     owner_id: str
+    type: str
 
 
 class ArticleRawModel(DataModel):
+    entry_id: str
     platform: str
     link: str
     content: dict
     author_id: str
+    type: str
 
 
 class PostsRawModel(DataModel):
+    entry_id: str
     platform: str
     content: dict
-    author_id: str | None = None
+    author_id: str
     image: Optional[str] = None
+    type: str
diff --git a/course/module-3/poetry.lock b/course/module-3/poetry.lock
new file mode 100644
index 0000000..50e7b29
--- /dev/null
+++ b/course/module-3/poetry.lock
@@ -0,0 +1,3750 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
+[[package]]
+name = "aiohttp"
+version = "3.9.5"
+description = "Async http client/server framework (asyncio)"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
+    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
+    {file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"},
+    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"},
+    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"},
+    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8a02fbeca6f63cb1f0475c799679057fc9268b77075ab7cf3f1c600e81dd46b"},
+    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c26959ca7b75ff768e2776d8055bf9582a6267e24556bb7f7bd29e677932be72"},
+    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:714d4e5231fed4ba2762ed489b4aec07b2b9953cf4ee31e9871caac895a839c0"},
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+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10, <3.12"
+content-hash = "5fbbde1409f32472af9dd6348c5c39e8f4bcb003ffaf3e1820b41dba8d4cfd9e"
diff --git a/course/module-3/pyproject.toml b/course/module-3/pyproject.toml
new file mode 100644
index 0000000..e17aa39
--- /dev/null
+++ b/course/module-3/pyproject.toml
@@ -0,0 +1,28 @@
+[tool.poetry]
+name = "rag-system"
+description = ""
+version = "0.1.0"
+authors = ["vlad_adu <vladadumitracesei02@gmail.com>"]
+readme = "README.md"
+
+
+[tool.poetry.dependencies]
+python = ">=3.10, <3.12"
+pydantic = "^2.6.3"
+pydantic-settings = "^2.1.0"
+bytewax = "^0.18.2"
+pika = "^1.3.2"
+qdrant-client = "^1.8.0"
+unstructured = "^0.12.6"
+langchain = "^0.1.13"
+sentence-transformers = "^2.6.1"
+instructorembedding = "^1.0.1"
+numpy = "^1.26.4"
+langchain-openai = "^0.1.3"
+bson = "^0.5.10"
+pymongo = "^4.6.3"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/course/module-3/rag/query_expanison.py b/course/module-3/rag/query_expanison.py
new file mode 100644
index 0000000..18d387c
--- /dev/null
+++ b/course/module-3/rag/query_expanison.py
@@ -0,0 +1,22 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import QueryExpansionTemplate
+from settings import settings
+
+
+class QueryExpansion:
+    @staticmethod
+    def generate_response(query: str) -> list[str]:
+        prompt = QueryExpansionTemplate(question=query).create_template()
+        model = ChatOpenAI(model=settings.OPENAI_MODEL_ID, temperature=0)
+
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="expanded_queries", template=prompt
+        )
+
+        response = chain.invoke({"question": query})
+        result = response["expanded_queries"]
+        queries = result.strip().split("\n")
+        cleaned_queries = [item.strip() for item in queries if item.strip()]
+        return cleaned_queries
diff --git a/course/module-3/rag/reranking.py b/course/module-3/rag/reranking.py
new file mode 100644
index 0000000..93f880c
--- /dev/null
+++ b/course/module-3/rag/reranking.py
@@ -0,0 +1,19 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import RerankingTemplate
+
+
+class Reranker:
+    @staticmethod
+    def generate_response(query: str, passages: str) -> list[str]:
+        prompt = RerankingTemplate(question=query, passages=passages).create_template()
+        model = ChatOpenAI(model="gpt-4-1106-preview")
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="rerank", template=prompt
+        )
+
+        response = chain.invoke({"question": query, "passages": passages})
+        result = response["rerank"]
+
+        return result
diff --git a/course/module-3/rag/retriever.py b/course/module-3/rag/retriever.py
new file mode 100644
index 0000000..c231ef9
--- /dev/null
+++ b/course/module-3/rag/retriever.py
@@ -0,0 +1,109 @@
+import concurrent.futures
+
+import utils
+from qdrant_client import QdrantClient, models
+from rag.query_expanison import QueryExpansion
+from rag.reranking import Reranker
+from rag.self_query import SelfQuery
+from sentence_transformers.SentenceTransformer import SentenceTransformer
+from settings import settings
+
+
+class VectorRetriever:
+    """
+    Class for retrieving vectors from a Vector store in a RAG system using query expansion and Multitenancy search.
+    """
+
+    def __init__(self, query: str):
+        self._client = QdrantClient(
+            host=settings.QDRANT_DATABASE_HOST, port=settings.QDRANT_DATABASE_PORT
+        )
+        self.query = query
+        self._embedder = SentenceTransformer(settings.EMBEDDING_MODEL_ID)
+        self._query_expander = QueryExpansion()
+        self._metadata_extractor = SelfQuery()
+        self._reranker = Reranker()
+
+    def _search_single_query(
+        self, generated_query: str, metadata_filter_value: str, k: int
+    ):
+        vectors = [
+            self._client.search(
+                collection_name="vector_posts",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="author_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+            self._client.search(
+                collection_name="vector_articles",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="author_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+            self._client.search(
+                collection_name="vector_repositories",
+                query_filter=models.Filter(
+                    must=[
+                        models.FieldCondition(
+                            key="owner_id",
+                            match=models.MatchValue(
+                                value=metadata_filter_value,
+                            ),
+                        )
+                    ]
+                ),
+                query_vector=self._embedder.encode(generated_query).tolist(),
+                limit=k,
+            ),
+        ]
+
+        return utils.flatten(vectors)
+
+    def retrieve_top_k(self, k: int) -> list:
+        generated_queries = self._query_expander.generate_response(self.query)
+        metadata_filter_value = self._metadata_extractor.generate_response(self.query)
+
+        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
+            search_tasks = [
+                executor.submit(
+                    self._search_single_query, query, metadata_filter_value, k
+                )
+                for query in generated_queries
+            ]
+
+            hits = [
+                task.result() for task in concurrent.futures.as_completed(search_tasks)
+            ]
+            hits = utils.flatten(hits)
+
+            return hits
+
+    def rerank(self, hits: list) -> list[str]:
+        content_list = [hit.payload["content"] for hit in hits]
+        passages = "\n".join(content_list)
+        rerank_hits = self._reranker.generate_response(
+            query=self.query, passages=passages
+        )
+
+        return rerank_hits
+
+    def set_query(self, query: str):
+        self.query = query
diff --git a/course/module-3/rag/self_query.py b/course/module-3/rag/self_query.py
new file mode 100644
index 0000000..9aebf88
--- /dev/null
+++ b/course/module-3/rag/self_query.py
@@ -0,0 +1,19 @@
+from langchain_openai import ChatOpenAI
+
+from llm_components.chain import GeneralChain
+from llm_components.prompt_templates import SelfQueryTemplate
+
+
+class SelfQuery:
+    @staticmethod
+    def generate_response(query) -> str:
+        prompt = SelfQueryTemplate(question=query).create_template()
+        model = ChatOpenAI(model="gpt-4-1106-preview", temperature=0)
+
+        chain = GeneralChain().get_chain(
+            llm=model, output_key="metadata_filter_value", template=prompt
+        )
+
+        response = chain.invoke({"question": query})
+        result = response["metadata_filter_value"]
+        return result
diff --git a/course/module-3/retriever.py b/course/module-3/retriever.py
new file mode 100644
index 0000000..030ce03
--- /dev/null
+++ b/course/module-3/retriever.py
@@ -0,0 +1,18 @@
+from dotenv import load_dotenv
+
+from rag.retriever import VectorRetriever
+
+if __name__ == "__main__":
+    load_dotenv()
+    query = """
+        Hello my id is: dbe92510-c33f-4ff7-9908-ee6356fe251f
+        Could you please draft a LinkedIn post discussing RAG systems?
+        I'm particularly interested in highlighting how RAG systems streamline project management processes and provide a clear visual representation of task statuses. 
+        Additionally, if you could touch on any recent advancements or best practices in utilizing RAG systems for efficient project tracking, that would be fantastic. 
+        Looking forward to sharing valuable insights with my professional network! 
+        """
+    retriever = VectorRetriever(query=query)
+    hits = retriever.retrieve_top_k(5)
+    print(hits)
+    result = retriever.rerank(hits=hits)
+    print(result)
diff --git a/course/module-3/settings.py b/course/module-3/settings.py
new file mode 100644
index 0000000..05d3613
--- /dev/null
+++ b/course/module-3/settings.py
@@ -0,0 +1,37 @@
+from typing import Optional
+
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class AppSettings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+
+    # Embeddings config
+    EMBEDDING_MODEL_ID: str = "sentence-transformers/all-MiniLM-L6-v2"
+    EMBEDDING_MODEL_MAX_INPUT_LENGTH: int = 256
+    EMBEDDING_SIZE: int = 384
+    EMBEDDING_MODEL_DEVICE: str = "cpu"
+
+    OPENAI_MODEL_ID: str = "gpt-4-1106-preview"
+    OPENAI_API_KEY: str = "None"
+
+    # MongoDB configs
+    MONGO_DATABASE_HOST: str = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"  # mongodb://127.0.0.1:27017/?replicaSet=rs0
+    MONGO_DATABASE_NAME: str = "scrabble"
+
+    # QdrantDB config
+    QDRANT_DATABASE_HOST: str = "localhost"
+    QDRANT_DATABASE_PORT: int = 6333
+    QDRANT_DATABASE_URL: str = "http://localhost:6333"
+
+    CLEANED_DATA_OUTPUT_COLLECTION_NAME: str = "cleaned_posts"
+    QDRANT_APIKEY: Optional[str] = None
+
+    # MQ config
+    RABBITMQ_DEFAULT_USERNAME: str = "guest"
+    RABBITMQ_DEFAULT_PASSWORD: str = "guest"
+    RABBITMQ_HOST: str = "localhost"
+    RABBITMQ_PORT: int = 5673
+
+
+settings = AppSettings()
diff --git a/src/bonus_superlinked_rag/utils/__init__.py b/course/module-3/utils/__init__.py
similarity index 100%
rename from src/bonus_superlinked_rag/utils/__init__.py
rename to course/module-3/utils/__init__.py
diff --git a/src/feature_pipeline/utils/chunking.py b/course/module-3/utils/chunking.py
similarity index 95%
rename from src/feature_pipeline/utils/chunking.py
rename to course/module-3/utils/chunking.py
index bdc4980..856cad9 100644
--- a/src/feature_pipeline/utils/chunking.py
+++ b/course/module-3/utils/chunking.py
@@ -3,7 +3,7 @@
     SentenceTransformersTokenTextSplitter,
 )
 
-from config import settings
+from settings import settings
 
 
 def chunk_text(text: str) -> list[str]:
diff --git a/src/bonus_superlinked_rag/utils/cleaning.py b/course/module-3/utils/cleaning.py
similarity index 100%
rename from src/bonus_superlinked_rag/utils/cleaning.py
rename to course/module-3/utils/cleaning.py
diff --git a/src/feature_pipeline/utils/embeddings.py b/course/module-3/utils/embeddings.py
similarity index 93%
rename from src/feature_pipeline/utils/embeddings.py
rename to course/module-3/utils/embeddings.py
index 97bba46..811f0cd 100644
--- a/src/feature_pipeline/utils/embeddings.py
+++ b/course/module-3/utils/embeddings.py
@@ -1,7 +1,7 @@
 from InstructorEmbedding import INSTRUCTOR
 from sentence_transformers.SentenceTransformer import SentenceTransformer
 
-from config import settings
+from settings import settings
 
 
 def embedd_text(text: str):
diff --git a/data/links.txt b/data/links.txt
deleted file mode 100644
index 0a24003..0000000
--- a/data/links.txt
+++ /dev/null
@@ -1,50 +0,0 @@
-https://medium.com/decodingml/an-end-to-end-framework-for-production-ready-llm-systems-by-building-your-llm-twin-2cc6bb01141f
-https://medium.com/decodingml/a-real-time-retrieval-system-for-rag-on-social-media-data-9cc01d50a2a0
-https://medium.com/decodingml/sota-python-streaming-pipelines-for-fine-tuning-llms-and-rag-in-real-time-82eb07795b87
-https://medium.com/decodingml/the-4-advanced-rag-algorithms-you-must-know-to-implement-5d0c7f1199d2
-https://medium.com/decodingml/architect-scalable-and-cost-effective-llm-rag-inference-pipelines-73b94ef82a99
-https://decodingml.substack.com/p/real-time-feature-pipelines-with?r=1ttoeh
-https://decodingml.substack.com/p/building-ml-systems-the-right-way?r=1ttoeh
-https://decodingml.substack.com/p/reduce-your-pytorchs-code-latency?r=1ttoeh
-https://decodingml.substack.com/p/llm-agents-demystified?r=1ttoeh
-https://decodingml.substack.com/p/scalable-rag-ingestion-pipeline-using?r=1ttoeh
-https://decodingml.substack.com/p/the-ultimate-mlops-tool?r=1ttoeh
-https://decodingml.substack.com/p/the-new-king-of-infrastructure-as?r=1ttoeh
-https://decodingml.substack.com/p/highly-scalable-data-ingestion-architecture?r=1ttoeh
-https://decodingml.substack.com/p/2-key-llmops-concepts?r=1ttoeh
-https://decodingml.substack.com/p/the-llm-twin-free-course-on-production?r=1ttoeh
-https://decodingml.substack.com/p/a-blueprint-for-designing-production?r=1ttoeh
-https://decodingml.substack.com/p/the-difference-between-development?r=1ttoeh
-https://decodingml.substack.com/p/architect-scalable-and-cost-effective?r=1ttoeh
-https://decodingml.substack.com/p/7-tips-to-reduce-your-vram-when-training?r=1ttoeh
-https://decodingml.substack.com/p/using-this-python-package-you-can?r=1ttoeh
-https://decodingml.substack.com/p/the-4-advanced-rag-algorithms-you?r=1ttoeh
-https://decodingml.substack.com/p/problems-deploying-your-ml-models?r=1ttoeh
-https://decodingml.substack.com/p/sota-python-streaming-pipelines-for?r=1ttoeh
-https://decodingml.substack.com/p/ready-for-production-ml-here-are?r=1ttoeh
-https://decodingml.substack.com/p/my-ml-monthly-learning-resource-recommendations?r=1ttoeh
-https://decodingml.substack.com/p/an-end-to-end-framework-for-production?r=1ttoeh
-https://decodingml.substack.com/p/upskill-your-llm-knowledge-base-with?r=1ttoeh
-https://decodingml.substack.com/p/want-to-learn-an-end-to-end-framework?r=1ttoeh
-https://decodingml.substack.com/p/my-favorite-way-to-implement-a-configuration?r=1ttoeh
-https://decodingml.substack.com/p/a-real-time-retrieval-system-for?r=1ttoeh
-https://decodingml.substack.com/p/4-key-decoding-strategies-for-llms?r=1ttoeh
-https://decodingml.substack.com/p/dml-new-year-the-new-and-improved?r=1ttoeh
-https://decodingml.substack.com/p/dml-8-types-of-mlops-tools-that-must?r=1ttoeh
-https://decodingml.substack.com/p/dml-this-is-what-you-need-to-build?r=1ttoeh
-https://decodingml.substack.com/p/dml-7-steps-on-how-to-fine-tune-an?r=1ttoeh
-https://decodingml.substack.com/p/dml-how-do-you-generate-a-q-and-a?r=1ttoeh
-https://decodingml.substack.com/p/dml-what-do-you-need-to-fine-tune?r=1ttoeh
-https://decodingml.substack.com/p/dml-why-and-when-do-you-need-to-fine?r=1ttoeh
-https://decodingml.substack.com/p/dml-how-to-implement-a-streaming?r=1ttoeh
-https://decodingml.substack.com/p/dml-why-and-what-do-you-need-a-streaming?r=1ttoeh
-https://decodingml.substack.com/p/dml-unwrapping-the-3-pipeline-design?r=1ttoeh
-https://decodingml.substack.com/p/dml-how-to-design-an-llm-system-for?r=1ttoeh
-https://decodingml.substack.com/p/dml-synced-vector-dbs-a-guide-to?r=1ttoeh
-https://decodingml.substack.com/p/dml-what-is-the-difference-between?r=1ttoeh
-https://decodingml.substack.com/p/dml-7-steps-to-build-a-production?r=1ttoeh
-https://decodingml.substack.com/p/dml-chain-of-thought-reasoning-write?r=1ttoeh
-https://decodingml.substack.com/p/dml-build-and-serve-a-production?r=1ttoeh
-https://decodingml.substack.com/p/dml-4-key-ideas-you-must-know-to?r=1ttoeh
-https://decodingml.substack.com/p/dml-how-to-add-real-time-monitoring?r=1ttoeh
-https://decodingml.substack.com/p/dml-top-6-ml-platform-features-you?r=1ttoeh
\ No newline at end of file
diff --git a/docker-compose-superlinked.yml b/docker-compose-superlinked.yml
deleted file mode 100644
index 86efe1c..0000000
--- a/docker-compose-superlinked.yml
+++ /dev/null
@@ -1,120 +0,0 @@
-services:
-  mongo1:
-    image: mongo:5
-    container_name: llm-twin-mongo1
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30001"]
-    volumes:
-      - mongo-replica-1-data:/data/db
-    ports:
-      - "30001:30001"
-    healthcheck:
-      test: test $$(echo "rs.initiate({_id:'my-replica-set',members:[{_id:0,host:\"mongo1:30001\"},{_id:1,host:\"mongo2:30002\"},{_id:2,host:\"mongo3:30003\"}]}).ok || rs.status().ok" | mongo --port 30001 --quiet) -eq 1
-      interval: 10s
-      start_period: 30s
-    restart: always
-    networks:
-        - server_default
-
-  mongo2:
-    image: mongo:5
-    container_name: llm-twin-mongo2
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30002"]
-    volumes:
-      - mongo-replica-2-data:/data/db
-    ports:
-      - "30002:30002"
-    restart: always
-    networks:
-        - server_default
-
-  mongo3:
-    image: mongo:5
-    container_name: llm-twin-mongo3
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30003"]
-    volumes:
-      - mongo-replica-3-data:/data/db
-    ports:
-      - "30003:30003"
-    restart: always
-    networks:
-        - server_default
-
-  mq:
-    image: rabbitmq:3-management-alpine
-    container_name: llm-twin-mq
-    ports:
-      - "5672:5672"
-      - "15672:15672"
-    volumes:
-      - ./rabbitmq/data/:/var/lib/rabbitmq/
-      - ./rabbitmq/log/:/var/log/rabbitmq
-    healthcheck:
-      test: ["CMD", "rabbitmqctl", "ping"]
-      interval: 30s
-      timeout: 10s
-      retries: 5
-    restart: always
-    networks:
-        - server_default
-
-  data-crawlers:
-    image: "llm-twin-data-crawlers"
-    container_name: llm-twin-data-crawlers
-    platform: "linux/amd64"
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.data_crawlers
-    env_file:
-      - .env
-    ports:
-      - "9010:8080"
-    depends_on:
-      - mongo1
-      - mongo2
-      - mongo3
-    networks:
-      - server_default
-
- data_cdc:
-    image: "llm-twin-data-cdc"
-    container_name: llm-twin-data-cdc
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.data_cdc
-    env_file:
-      - .env
-    depends_on:
-      - mongo1
-      - mongo2
-      - mongo3
-      - mq
-    networks:
-      - server_default
-
-  feature_pipeline:
-    image: "llm-twin-feature-pipeline"
-    container_name: llm-twin-feature-pipeline
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.feature_pipeline
-    environment:
-      BYTEWAX_PYTHON_FILE_PATH: "main:flow"
-      DEBUG: "false"
-      BYTEWAX_KEEP_CONTAINER_ALIVE: "false"
-    env_file:
-      - .env
-    depends_on:
-      - mq
-    restart: always
-    networks:
-      - server_default
-
-volumes:
-  mongo-replica-1-data:
-  mongo-replica-2-data:
-  mongo-replica-3-data:
-
-networks:
-  server_default:
-    external: true
-  
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
index 539b6a0..6b0896f 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,112 +1,26 @@
-services:
-  mongo1:
-    image: mongo:5
-    container_name: llm-twin-mongo1
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30001"]
-    volumes:
-      - mongo-replica-1-data:/data/db
-    ports:
-      - "30001:30001"
-    healthcheck:
-      test: test $$(echo "rs.initiate({_id:'my-replica-set',members:[{_id:0,host:\"mongo1:30001\"},{_id:1,host:\"mongo2:30002\"},{_id:2,host:\"mongo3:30003\"}]}).ok || rs.status().ok" | mongo --port 30001 --quiet) -eq 1
-      interval: 10s
-      start_period: 30s
-    restart: always
+version: '3.8'
 
-  mongo2:
-    image: mongo:5
-    container_name: llm-twin-mongo2
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30002"]
-    volumes:
-      - mongo-replica-2-data:/data/db
-    ports:
-      - "30002:30002"
-    restart: always
-
-  mongo3:
-    image: mongo:5
-    container_name: llm-twin-mongo3
-    command: ["--replSet", "my-replica-set", "--bind_ip_all", "--port", "30003"]
-    volumes:
-      - mongo-replica-3-data:/data/db
-    ports:
-      - "30003:30003"
-    restart: always
-
-  mq:
-    image: rabbitmq:3-management-alpine
-    container_name: llm-twin-mq
-    ports:
-      - "5673:5672"
-      - "15673:15672"
-    volumes:
-      - ./rabbitmq/data/:/var/lib/rabbitmq/
-      - ./rabbitmq/log/:/var/log/rabbitmq
-    restart: always
-
-  qdrant:
-    image: qdrant/qdrant:latest
-    container_name: llm-twin-qdrant
+services:
+  mongo:
+    image: mongo
+    container_name: "decodingml_mongo"
+    logging:
+      options:
+        max-size: 1g
+    environment:
+      MONGO_INITDB_ROOT_USERNAME: "decodingml"
+      MONGO_INITDB_ROOT_PASSWORD: "decodingml"
     ports:
-      - "6333:6333"
-      - "6334:6334"
-    expose:
-      - "6333"
-      - "6334"
-      - "6335"
+      - '27017:27017'
     volumes:
-      - qdrant-data:/qdrant_data
-    restart: always
-
-  data-crawlers:
-    image: "llm-twin-data-crawlers"
-    container_name: llm-twin-data-crawlers
-    platform: "linux/amd64"
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.data_crawlers
-    env_file:
-      - .env
-    ports:
-      - "9010:8080"
-    depends_on:
-      - mongo1
-      - mongo2
-      - mongo3
-
-  data_cdc:
-    image: "llm-twin-data-cdc"
-    container_name: llm-twin-data-cdc
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.data_cdc
-    env_file:
-      - .env
-    depends_on:
-      - mongo1
-      - mongo2
-      - mongo3
-      - mq
-
-  feature_pipeline:
-    image: "llm-twin-feature-pipeline"
-    container_name: llm-twin-feature-pipeline
-    build:
-      context: .
-      dockerfile: .docker/Dockerfile.feature_pipeline
-    environment:
-      BYTEWAX_PYTHON_FILE_PATH: "main:flow"
-      DEBUG: "false"
-      BYTEWAX_KEEP_CONTAINER_ALIVE: "true"
-    env_file:
-      - .env
-    depends_on:
-      - mq
-      - qdrant
+      - mongo_data:/data/db
+    networks:
+      - local
     restart: always
 
 volumes:
-  mongo-replica-1-data:
-  mongo-replica-2-data:
-  mongo-replica-3-data:
-  qdrant-data:
+  mongo_data:
+
+networks:
+  local:
+    driver: bridge
\ No newline at end of file
diff --git a/media/architecture.png b/media/architecture.png
index cf1a6af..cb6cb8c 100644
Binary files a/media/architecture.png and b/media/architecture.png differ
diff --git a/media/fine-tuning-workflow.png b/media/fine-tuning-workflow.png
deleted file mode 100644
index 4d070e7..0000000
Binary files a/media/fine-tuning-workflow.png and /dev/null differ
diff --git a/media/llm_engineers_handbook_cover.png b/media/llm_engineers_handbook_cover.png
deleted file mode 100644
index 365dda1..0000000
Binary files a/media/llm_engineers_handbook_cover.png and /dev/null differ
diff --git a/media/qdrant-example.png b/media/qdrant-example.png
deleted file mode 100644
index 3aa75a8..0000000
Binary files a/media/qdrant-example.png and /dev/null differ
diff --git a/media/sponsors/bytewax.png b/media/sponsors/bytewax.png
deleted file mode 100644
index f297cb1..0000000
Binary files a/media/sponsors/bytewax.png and /dev/null differ
diff --git a/media/sponsors/comet.png b/media/sponsors/comet.png
deleted file mode 100644
index cb9c7a8..0000000
Binary files a/media/sponsors/comet.png and /dev/null differ
diff --git a/media/sponsors/opik.svg b/media/sponsors/opik.svg
deleted file mode 100644
index 9914a1e..0000000
--- a/media/sponsors/opik.svg
+++ /dev/null
@@ -1,14 +0,0 @@
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diff --git a/media/ui-example.png b/media/ui-example.png
deleted file mode 100644
index 8dcd731..0000000
Binary files a/media/ui-example.png and /dev/null differ
diff --git a/ops/.gitignore b/ops/.gitignore
new file mode 100644
index 0000000..c695889
--- /dev/null
+++ b/ops/.gitignore
@@ -0,0 +1,2 @@
+/bin/
+/node_modules/
diff --git a/ops/Pulumi.yaml b/ops/Pulumi.yaml
new file mode 100644
index 0000000..c42bcf5
--- /dev/null
+++ b/ops/Pulumi.yaml
@@ -0,0 +1,7 @@
+name: decodingml
+runtime: nodejs
+description: AWS Cloud Infrastructure for Twin LMM Course
+config:
+  pulumi:tags:
+    value:
+      pulumi:template: ""
diff --git a/ops/components/config.ts b/ops/components/config.ts
new file mode 100644
index 0000000..a8b6419
--- /dev/null
+++ b/ops/components/config.ts
@@ -0,0 +1,6 @@
+export const SubnetCidrBlocks = {
+    Internet: '0.0.0.0/0',
+    VPC: '10.0.0.0/16',
+    PublicOne: '10.0.0.0/20',
+    PublicTwo: '10.0.16.0/20',
+} as const;
diff --git a/ops/components/crawler.ts b/ops/components/crawler.ts
new file mode 100644
index 0000000..51cb395
--- /dev/null
+++ b/ops/components/crawler.ts
@@ -0,0 +1,79 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+
+export interface CrawlerProps {
+    vpcId: pulumi.Input<string>
+    timeout: pulumi.Input<number>
+    memory: pulumi.Input<number>
+}
+
+export class Crawler extends pulumi.ComponentResource {
+    public readonly arn: pulumi.Output<string>
+
+    constructor (
+        name: string,
+        props: CrawlerProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:Crawler", name, {}, opts);
+
+        const accountId = pulumi.output(aws.getCallerIdentity()).accountId;
+        const region = pulumi.output(aws.getRegion()).name;
+
+        const lambdaExecutionRole = new aws.iam.Role(`${name}-role`, {
+            assumeRolePolicy: JSON.stringify({
+                Version: "2012-10-17",
+                Statement: [{
+                    Effect: "Allow",
+                    Principal: {
+                        Service: "lambda.amazonaws.com",
+                    },
+                    Action: "sts:AssumeRole",
+                }],
+            }),
+            managedPolicyArns: [
+                aws.iam.ManagedPolicy.AmazonS3FullAccess,
+                aws.iam.ManagedPolicy.AmazonDocDBFullAccess,
+                aws.iam.ManagedPolicy.AWSLambdaBasicExecutionRole,
+                aws.iam.ManagedPolicy.AWSLambdaVPCAccessExecutionRole,
+                aws.iam.ManagedPolicy.CloudWatchLambdaInsightsExecutionRolePolicy
+            ]
+        })
+
+        const sg = new aws.ec2.SecurityGroup(`${name}-security-group`, {
+            name: `${name}-sg`,
+            description: "Crawler Lambda Access",
+            vpcId: props.vpcId,
+            egress: [{
+                protocol: "-1",
+                description: "Allow all outbound traffic by default",
+                fromPort: 0,
+                toPort: 0,
+                cidrBlocks: ["0.0.0.0/0"],
+            }],
+            tags: {
+                Name: `${name}-sg`
+            }
+        })
+
+        const lambdaFunction = new aws.lambda.Function(`${name}-lambda-function`, {
+            name: `${name}`,
+            imageUri: pulumi.interpolate`${accountId}.dkr.ecr.${region}.amazonaws.com/crawler:latest`,
+            packageType: 'Image',
+            description: 'Crawler Lambda Function',
+            timeout: props.timeout,
+            memorySize: props.memory,
+            role: lambdaExecutionRole.arn,
+            vpcConfig: {
+                subnetIds: pulumi.output(aws.ec2.getSubnets({tags: {Type: 'public'}})).ids,
+                securityGroupIds: [sg.id],
+            }
+        }, {dependsOn: lambdaExecutionRole})
+
+        this.arn = lambdaFunction.arn
+
+        this.registerOutputs({
+            arn:  this.arn
+        })
+    }
+}
diff --git a/ops/components/docdb.ts b/ops/components/docdb.ts
new file mode 100644
index 0000000..df33d12
--- /dev/null
+++ b/ops/components/docdb.ts
@@ -0,0 +1,94 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+
+
+export interface DocumentDBClusterProps {
+    vpcId: pulumi.Input<string>
+    instanceClass?: pulumi.Input<string>
+    multiAZ?: pulumi.Input<boolean>
+    port?: pulumi.Input<number>
+
+    backupRetentionPeriod?: pulumi.Input<number>
+}
+
+export class DocumentDBCluster extends pulumi.ComponentResource {
+
+    constructor (
+        name: string,
+        props: DocumentDBClusterProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:DocumentDBCluster", name, {}, opts);
+
+        const username = pulumi.output(aws.ssm.getParameter({ name: `/${name}/cluster/master/username` })).value
+        const password = pulumi.output(aws.ssm.getParameter({ name: `/${name}/cluster/master/password` })).value
+
+        const subnetGroup = new aws.docdb.SubnetGroup(`${name}-docdb-subnet-group`, {
+            name: `${name}-cluster-subnet-group`,
+            description: `VPC subnet group for the ${name}-cluster`,
+            subnetIds: pulumi.output(aws.ec2.getSubnets({tags: {Type: 'public'}})).ids,
+            tags: {
+                Name: `${name}-cluster-subnet-group`
+            }
+        }, {parent: this})
+
+        const securityGroup = new aws.ec2.SecurityGroup(`${name}-docdb-sg`, {
+            name: `${name}-docdb-cluster-sg`,
+            description: "Database access",
+            vpcId: props.vpcId,
+            tags: {
+                Name: `${name}-docdb-cluster-sg`
+            },
+            ingress: [
+                {
+                    description: "Ingress from anywhere",
+                    fromPort: props.port || 27017,
+                    toPort: props.port || 27017,
+                    protocol: "-1",
+                },
+            ],
+            egress: [{
+                protocol: "-1",
+                description: "Allow all outbound traffic by default",
+                fromPort: 0,
+                toPort: 0,
+                cidrBlocks: ["0.0.0.0/0"],
+            }],
+        }, {parent: this})
+
+        const cluster = new aws.docdb.Cluster(`${name}-docdb-cluster`, {
+            // availabilityZones:  pulumi.output(aws.getAvailabilityZones({state: "available"}) if props.multiAZ else
+            backupRetentionPeriod: props.backupRetentionPeriod || 7,
+            clusterIdentifier: `${name}-cluster`,
+            masterUsername: username,
+            masterPassword: password,
+            engineVersion: "5.0.0",
+            port: props.port || 27017,
+            dbSubnetGroupName: subnetGroup.name,
+            storageEncrypted: true,
+            skipFinalSnapshot: true,
+            vpcSecurityGroupIds: [ securityGroup.id ],
+            tags: {
+                Name: `${name}-cluster`
+            }
+        }, {parent: this})
+
+        const primaryInstance = new aws.docdb.ClusterInstance(`${name}-docdb-primary-instance`, {
+            clusterIdentifier: cluster.clusterIdentifier,
+            identifier: `${name}-primary-instance`,
+            instanceClass: props.instanceClass || "db.t3.medium",
+            tags: {
+                Name: `${name}-primary-instance`
+            }
+        }, {parent: this})
+
+        const hostSSMParameter = new aws.ssm.Parameter(`${name}-mq-broker-host-ssm-parameter`, {
+            name: `/${name}/cluster/host`,
+            type: aws.ssm.ParameterType.String,
+            description: `DocDB database host for ${name}-cluster`,
+            value: pulumi.all([cluster.endpoint, cluster.port]).apply(([endpoint, port]) =>
+                `mongodb://${username}:${password}@${endpoint}:${port}/?replicaSet=rs0&readPreference=secondaryPreferred&retryWrites=false`
+            ),
+        }, {parent: this})
+    }
+}
diff --git a/ops/components/ecs/cluster.ts b/ops/components/ecs/cluster.ts
new file mode 100644
index 0000000..581069c
--- /dev/null
+++ b/ops/components/ecs/cluster.ts
@@ -0,0 +1,62 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+
+
+export interface ECSClusterProps {
+    vpcId: pulumi.Input<string>
+}
+
+export class ECSCluster extends pulumi.ComponentResource {
+    name: pulumi.Output<string>
+
+    constructor (
+        name: string,
+        props: ECSClusterProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:ECSCluster", name, {}, opts);
+
+        const cluster = new aws.ecs.Cluster(`${name}-cluster`, {
+            name: `${name}-cluster`,
+        }, {parent: this})
+
+        this.name = cluster.name
+
+        const securityGroup = new aws.ec2.SecurityGroup(`${name}-sg`, {
+            name: `${name}-ecs-host-sg`,
+            description: 'Access to the ECS hosts that run containers',
+            vpcId: props.vpcId,
+            ingress: [
+                {
+                    description: "Ingress from other containers in the same security group",
+                    fromPort: 0,
+                    toPort: 0,
+                    protocol: "-1",
+                    self: true,
+                }
+            ],
+            egress: [
+                {
+                    cidrBlocks: ['0.0.0.0/0'],
+                    description: "Allow all outbound traffic by default",
+                    protocol: "-1",
+                    fromPort: 0,
+                    toPort: 0,
+                },
+            ],
+            tags: {
+                Name: `${name}-ecs-host-sg`
+            }
+        }, {parent: this})
+
+        new aws.servicediscovery.PrivateDnsNamespace(`${name}-private-dns-namespace`, {
+            name: `${name}.internal`,
+            vpc: props.vpcId,
+        }, {parent: this})
+
+        this.registerOutputs({
+            name: this.name
+        })
+
+    }
+}
\ No newline at end of file
diff --git a/ops/components/ecs/iam.ts b/ops/components/ecs/iam.ts
new file mode 100644
index 0000000..b442bf7
--- /dev/null
+++ b/ops/components/ecs/iam.ts
@@ -0,0 +1,69 @@
+import * as aws from "@pulumi/aws";
+
+
+export const ecsRole = new aws.iam.Role("ecs-role", {
+    name: `ecs-role`,
+    assumeRolePolicy: aws.iam.assumeRolePolicyForPrincipal({ Service: "ecs.amazonaws.com" }),
+    path: "/",
+    inlinePolicies: [{
+        name: "ecs-service",
+        policy: JSON.stringify({
+            Statement: [{
+                Action: [
+                    'ec2:AttachNetworkInterface',
+                    'ec2:CreateNetworkInterface',
+                    'ec2:CreateNetworkInterfacePermission',
+                    'ec2:DeleteNetworkInterface',
+                    'ec2:DeleteNetworkInterfacePermission',
+                    'ec2:Describe*',
+                    'ec2:DetachNetworkInterface',
+                    'elasticloadbalancing:DeregisterInstancesFromLoadBalancer',
+                    'elasticloadbalancing:DeregisterTargets',
+                    'elasticloadbalancing:Describe*',
+                    'elasticloadbalancing:RegisterInstancesWithLoadBalancer',
+                    'elasticloadbalancing:RegisterTargets'
+                ],
+                Effect: 'Allow',
+                Resource: '*'
+            }],
+            Version: '2012-10-17',
+        } as aws.iam.PolicyDocument)
+    }]
+})
+
+
+export const ecsTaskExecutionRole = new aws.iam.Role("ecs-task-execution-role", {
+    name: `ecs-task-execution-role`,
+    assumeRolePolicy: aws.iam.assumeRolePolicyForPrincipal({ Service: "ecs-tasks.amazonaws.com" }),
+    path: "/",
+    inlinePolicies: [
+        {
+            name: "ecs-logs",
+            policy: JSON.stringify({
+                Statement: [{
+                    Action: [
+                        'logs:CreateLogGroup'
+                    ],
+                    Effect: 'Allow',
+                    Resource: '*'
+                }]
+            } as aws.iam.PolicyDocument),
+        },
+        {
+            name: "ecs-ssm",
+            policy: JSON.stringify({
+                Statement: [{
+                    Sid: "readEnvironmentParameters",
+                    Action: [
+                        'ssm:GetParameters'
+                    ],
+                    Effect: 'Allow',
+                    Resource: "*"
+                }]
+            } as aws.iam.PolicyDocument),
+        }
+    ],
+    managedPolicyArns: [
+        'arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy'
+    ]
+})
\ No newline at end of file
diff --git a/ops/components/ecs/service.ts b/ops/components/ecs/service.ts
new file mode 100644
index 0000000..f36ec8c
--- /dev/null
+++ b/ops/components/ecs/service.ts
@@ -0,0 +1,128 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+
+export interface ContainerSecrets {
+    name: pulumi.Input<string>;
+    parameter: pulumi.Input<string>;
+}
+
+export interface ServiceProps {
+    vpcId: pulumi.Input<string>
+
+    cluster: pulumi.Input<string>;
+    environment?: pulumi.Input<pulumi.Input<aws.ecs.KeyValuePair[]>>;
+    secrets: ContainerSecrets[];
+
+    command?: pulumi.Input<string[]>;
+    imageTag?: pulumi.Input<string>;
+    containerPort: pulumi.Input<number>;
+    containerCpu?: pulumi.Input<string>;
+    containerMemory?: pulumi.Input<string>;
+
+    deploymentController?: pulumi.Input<string>;
+
+    desiredCount?: pulumi.Input<number>;
+    role?: pulumi.Input<string>;
+}
+
+export class Service extends pulumi.ComponentResource {
+    constructor (
+        name: string,
+        props: ServiceProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+
+        super("decodingml:main:Service", name, {}, opts);
+
+        const accountId = pulumi.output(aws.getCallerIdentity()).accountId;
+        const region = pulumi.output(aws.getRegion()).name;
+
+        const imageUrl = pulumi.interpolate`${accountId}.dkr.ecr.${region}.amazonaws.com/chamberlain:latest`
+
+        const containerSecrets =  props.secrets.map(secret => {
+            return {
+              name: secret.name,
+              valueFrom: pulumi.interpolate`arn:aws:ssm:${region}:${accountId}:parameter/${secret.parameter}`
+            } as aws.ecs.Secret;
+        })
+
+        const logGroup = new aws.cloudwatch.LogGroup(`log-group`, {
+            name: `/ecs/${props.cluster}/${name}`,
+            retentionInDays: 90,
+            tags: {
+                Name: `${props.cluster}-${name}-cluster-log-group`
+            }
+        })
+
+        const taskDefinition = new aws.ecs.TaskDefinition(`${name}-ecs-task-definition`, {
+            family: name,
+            networkMode: 'awsvpc',
+            requiresCompatibilities: ["FARGATE"],
+            cpu: props.containerCpu || "512",
+            memory: props.containerMemory || "1024",
+            executionRoleArn: pulumi.output(aws.iam.getRole({name: `ecs-task-execution-role`})).arn,
+            taskRoleArn: props.role,
+            containerDefinitions: pulumi
+                .all([imageUrl, props.logGroup, props.environment, containerSecrets])
+                .apply(([image,logGroup,environment, secrets]) =>
+                    JSON.stringify([{
+                        name: name,
+                        image: image,
+                        portMappings: [{
+                            containerPort: props.containerPort,
+                        }],
+                        command: props.command,
+                        environment: environment,
+                        secrets: secrets,
+                        logConfiguration: {
+                            logDriver: "awslogs",
+                            options: {
+                                "awslogs-group": logGroup,
+                                "awslogs-create-group": "true",
+                                "awslogs-region": "eu-central-1",
+                                "awslogs-stream-prefix": name,
+                            },
+                        },
+                    } as aws.ecs.ContainerDefinition])
+                )
+        }, {parent: this})
+
+        const serviceDiscovery = new aws.servicediscovery.Service(`${name}-service-discovery`, {
+            name: name,
+            description: `Service discovery for ${name}`,
+            dnsConfig: {
+                routingPolicy: "MULTIVALUE",
+                dnsRecords: [{ type: "A", ttl: 60 }],
+                namespaceId: pulumi.output(aws.servicediscovery.getDnsNamespace({
+                    name: `streaming.internal`,
+                    type: 'DNS_PRIVATE',
+                })).id,
+            },
+            healthCheckCustomConfig: {
+                failureThreshold: 1
+            },
+        }, {parent: this})
+
+        new aws.ecs.Service(`${name}-ecs-service`, {
+            name: `${name}-service`,
+            cluster: props.cluster,
+            launchType: 'FARGATE',
+            deploymentController: {
+                type: props.deploymentController || "ECS",
+            },
+            desiredCount: props.desiredCount || 1,
+            taskDefinition: taskDefinition.arn,
+            serviceRegistries: {
+                registryArn: serviceDiscovery.arn,
+                containerName: `${name}`,
+            },
+            networkConfiguration: {
+                assignPublicIp: false,
+                securityGroups: pulumi.output(aws.ec2.getSecurityGroups({
+                    tags: {Name: `ecs-host-sg`}
+                })).ids,
+                subnets: pulumi.output(aws.ec2.getSubnets({tags: {Type: 'private'}})).ids
+            }
+        }, {parent: this})
+    }
+}
\ No newline at end of file
diff --git a/ops/components/mq.ts b/ops/components/mq.ts
new file mode 100644
index 0000000..3b300c8
--- /dev/null
+++ b/ops/components/mq.ts
@@ -0,0 +1,115 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+
+
+export interface MessageQueueBrokerProps {
+    vpcId: pulumi.Input<string>
+
+    engineVersion?: pulumi.Input<string>
+    instanceType?: pulumi.Input<string>
+
+}
+
+export class MessageQueueBroker extends pulumi.ComponentResource {
+
+    constructor(
+        name: string,
+        props: MessageQueueBrokerProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:MessageQueueBroker", name, {}, opts);
+
+        const accountId = pulumi.output(aws.getCallerIdentity()).accountId;
+        const region = pulumi.output(aws.getRegion()).name;
+
+        const securityGroup =  new aws.ec2.SecurityGroup(`${name}-mq-sg`, {
+            name: `${name}-mq-sg`,
+            description: "Message Queue broker access",
+            vpcId: props.vpcId,
+            ingress: [
+                {
+                    description: "Ingress from AMPQS protocol",
+                    fromPort: 5671,
+                    toPort: 5671,
+                    protocol: "tcp",
+                },
+                {
+                    description: "Ingress from HTTPS protocol",
+                    fromPort: 443,
+                    toPort: 443,
+                    protocol: "tcp",
+                },
+            ],
+            egress: [{
+                protocol: "-1",
+                description: "Allow all outbound traffic by default",
+                fromPort: 0,
+                toPort: 0,
+                cidrBlocks: ["0.0.0.0/0"],
+            }],
+            tags: {
+                Name: `${name}-mq-sg`
+            },
+        }, {parent: this})
+
+        const broker = new aws.mq.Broker(`${name}-mq-broker`, {
+            brokerName: `${name}-mq-broker`,
+            engineType: "RabbitMQ",
+            engineVersion: props.engineVersion || "3.11.20",
+            hostInstanceType: props.instanceType || "mq.t3.micro",
+            securityGroups: [securityGroup.id],
+            deploymentMode: "SINGLE_INSTANCE",
+            logs: {
+                general: true,
+            },
+            publiclyAccessible: true,
+            subnetIds: pulumi.output(aws.ec2.getSubnets({tags: {Type: 'public'}})).ids,
+            users: pulumi.all([
+                this.getSecretValue(`arn:aws:secretsmanager:${region}:${accountId}:secret:/${name}/broker/admin`),
+                this.getSecretValue(`arn:aws:secretsmanager:${region}:${accountId}:secret:/${name}/broker/replication-user`)
+            ]).apply(([adminSecret, replicationUserSecret]) => [
+                {
+                    username: JSON.parse(adminSecret).username,
+                    password: JSON.parse(adminSecret).password,
+                    consoleAccess: true,
+                },
+                {
+                    username: JSON.parse(replicationUserSecret).username,
+                    password: JSON.parse(replicationUserSecret).password,
+                    consoleAccess: true,
+                    replicationUser: true
+                }
+            ]),
+            tags: {
+                Name: `${name}-mq-sg`
+            },
+        }, {parent: this})
+
+        const hostSSMParameter = new aws.ssm.Parameter(`${name}-mq-broker-host-ssm-parameter`, {
+            name: `/${name}/broker/host`,
+            type: aws.ssm.ParameterType.String,
+            description: `RabbitMQ cluster host for ${name}-mq-broker`,
+            value: broker.instances[0].endpoints[0].apply(endpoint => {
+                return endpoint.split(":")[0];
+            }),
+        }, {parent: this})
+
+        const portSSMParameter = new aws.ssm.Parameter(`${name}-mq-broker-port-ssm-parameter`, {
+            name: `/${name}/broker/port`,
+            type: aws.ssm.ParameterType.String,
+            description: `RabbitMQ cluster port for ${name}-mq-broker`,
+            value: "5671",
+        }, {parent: this})
+    }
+
+    private async getSecretValue(secretName: string): Promise<pulumi.Output<string>> {
+        return pulumi.output(aws.secretsmanager.getSecretVersion({
+                secretId: secretName,
+            }, { async: true })).apply(secretVersion => {
+                if (!secretVersion.secretString) {
+                    throw new Error("Secret version contains no string data");
+                }
+                return secretVersion.secretString;
+            });
+    }
+}
\ No newline at end of file
diff --git a/ops/components/nat.ts b/ops/components/nat.ts
new file mode 100644
index 0000000..0a14e8b
--- /dev/null
+++ b/ops/components/nat.ts
@@ -0,0 +1,143 @@
+import * as pulumi from "@pulumi/pulumi";
+import * as aws from "@pulumi/aws";
+import {SubnetCidrBlocks} from "./config";
+
+export interface NatGatewayProps {
+    env: pulumi.Input<string>
+    vpcId: pulumi.Input<string>
+    subnet: pulumi.Input<string>
+
+    instanceImageAmiId?: pulumi.Input<string>
+}
+
+export class NatGateway extends pulumi.ComponentResource {
+    public readonly id: pulumi.Output<string>
+
+    constructor(
+        name: string,
+        props: NatGatewayProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:NatGateway", name, {}, opts);
+
+        const config = new pulumi.Config();
+
+        const sg = new aws.ec2.SecurityGroup(`${name}-security-group`, {
+            description: "Security Group for NAT Gateway",
+            ingress: [
+                {
+                    cidrBlocks: [SubnetCidrBlocks.VPC],
+                    description: "Allow all inbound traffic from network",
+                    protocol: "-1",
+                    fromPort: 0,
+                    toPort: 0,
+                },
+            ],
+            egress: [
+                {
+                    cidrBlocks: ['0.0.0.0/0'],
+                    description: "Allow all outbound traffic by default",
+                    protocol: "-1",
+                    fromPort: 0,
+                    toPort: 0,
+                },
+            ],
+            vpcId: props.vpcId,
+        }, {parent: this})
+
+        const iamRole = new aws.iam.Role(`${name}-role`, {
+            assumeRolePolicy: {
+                Version: '2012-10-17',
+                Statement: [
+                    {
+                      Action: ['sts:AssumeRole'],
+                      Effect: 'Allow',
+                      Principal: {
+                        Service: 'ec2.amazonaws.com',
+                      },
+                    },
+                ],
+            },
+            managedPolicyArns: [
+                `arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore`,
+            ],
+            inlinePolicies: [
+                {
+                    name: 'for-nat',
+                    policy: JSON.stringify({
+                      Statement: [
+                        {
+                          Action: [
+                            'ec2:AttachNetworkInterface',
+                            'ec2:ModifyNetworkInterfaceAttribute',
+                            'ec2:AssociateAddress',
+                            'ec2:DisassociateAddress',
+                            'ec2:*',
+                          ],
+                          Effect: 'Allow',
+                          Resource: '*',
+                        },
+                      ],
+                      Version: '2012-10-17',
+                    } as aws.iam.PolicyDocument),
+            },
+        ],
+
+        }, {parent: this})
+
+        const eni = new aws.ec2.NetworkInterface(`${name}-eni`, {
+            subnetId: props.subnet,
+            securityGroups: [sg.id],
+            sourceDestCheck: false,
+        }, {parent: this})
+
+        this.id = eni.id
+
+        const instanceProfile = new aws.iam.InstanceProfile(`${name}-instance-profile`, {
+            role: iamRole
+        }, {parent: this})
+
+        const launchTemplate = new aws.ec2.LaunchTemplate(`${name}-launch-template`, {
+            name: `pi-${props.env}-nat-launch-template`,
+            imageId: config.require('natInstanceImageId'),
+            instanceType: 't4g.nano',
+            iamInstanceProfile: { arn: instanceProfile.arn },
+            vpcSecurityGroupIds: [ sg.id ],
+            userData: eni.id.apply(id =>
+              Buffer.from(
+                [
+                  '#!/bin/bash',
+                  `echo "eni_id=${id}" >> /etc/fck-nat.conf`,
+                  'service fck-nat restart',
+                ].join('\n'),
+              ).toString('base64'),
+            ),
+            tags: {
+                Name: `pi-${props.env}-nat-launch-template`
+            },
+            tagSpecifications: [{
+                tags: {
+                    Name: `pi-${props.env}-nat-launch-template`
+                },
+                resourceType: 'instance'
+            }]
+        }, {dependsOn: instanceProfile, parent: this})
+
+
+        new aws.autoscaling.Group(`${name}-autoscaling-group`, {
+            maxSize: 1,
+            minSize: 1,
+            desiredCapacity: 1,
+            launchTemplate: {
+              id: launchTemplate.id,
+              version: '$Latest',
+            },
+            vpcZoneIdentifiers: [ props.subnet ],
+            tags: [{ key: 'Name', value: `pi-${props.env}-nat-instance-launch-template`, propagateAtLaunch: true }]
+        }, {parent: this})
+
+        this.registerOutputs({
+            id: this.id,
+        })
+    }
+}
\ No newline at end of file
diff --git a/ops/components/repository.ts b/ops/components/repository.ts
new file mode 100644
index 0000000..b92f3b5
--- /dev/null
+++ b/ops/components/repository.ts
@@ -0,0 +1,69 @@
+import * as pulumi from '@pulumi/pulumi'
+import * as aws from '@pulumi/aws'
+
+interface Props {}
+
+export class Repository extends pulumi.ComponentResource {
+  public name: pulumi.Output<string>
+  public arn: pulumi.Output<string>
+  public url: pulumi.Output<string>
+
+  public static dockerTags = {
+    github: 'github-crawler-latest',
+    linkedin: 'linkedin-crawler-latest',
+    medium: 'medium-crawler-latest',
+
+  } as const
+
+  private readonly tags = {
+    module: 'ai',
+    scope: 'ecr',
+  }
+
+  constructor(
+    name: string,
+    props: Props,
+    opts?: pulumi.ComponentResourceOptions,
+  ) {
+    super('deocingml:ai:ecr', name, {}, opts)
+
+    const ecr = new aws.ecr.Repository(
+      `${name}-repository`,
+      {
+        name,
+        tags: this.tags,
+        imageTagMutability: 'MUTABLE',
+      },
+      { parent: this },
+    )
+
+    new aws.ecr.LifecyclePolicy(
+      `${name}-lifecycle-policy`,
+      {
+        repository: ecr.name,
+        policy: {
+          rules: [
+            {
+              action: { type: 'expire' },
+              selection: {
+                tagStatus: 'untagged',
+                countNumber: 30,
+                countUnit: 'days',
+                countType: 'sinceImagePushed',
+              },
+              rulePriority: 1,
+              description: 'Delete older than 30 days images with no tag.',
+            },
+          ],
+        },
+      },
+      { parent: this },
+    )
+
+    this.arn = ecr.arn
+    this.name = ecr.name
+    this.url = ecr.repositoryUrl
+
+    this.registerOutputs()
+  }
+}
diff --git a/ops/components/vpc.ts b/ops/components/vpc.ts
new file mode 100644
index 0000000..05a059e
--- /dev/null
+++ b/ops/components/vpc.ts
@@ -0,0 +1,85 @@
+import * as pulumi from '@pulumi/pulumi'
+import * as aws from '@pulumi/aws'
+import {SubnetCidrBlocks} from "./config";
+
+interface VpcProps {}
+
+export class Vpc extends pulumi.ComponentResource {
+    public readonly id: pulumi.Output<string>
+
+    constructor(
+        name: string,
+        props: VpcProps,
+        opts?: pulumi.ComponentResourceOptions,
+    ) {
+        super("decodingml:main:Vpc", name, {}, opts);
+
+        const vpc = new aws.ec2.Vpc(`${name}-vpc`, {
+            cidrBlock: SubnetCidrBlocks.VPC,
+            enableDnsSupport: true,
+            enableDnsHostnames: true,
+            tags: {
+                Name: `${name}-vpc`,
+            },
+        }, { parent: this });
+
+        this.id = vpc.id
+
+        const azs = aws.getAvailabilityZones({
+            state: "available"
+        })
+
+        const publicSubnetOne = new aws.ec2.Subnet(`${name}-public-subnet-one`, {
+            vpcId: vpc.id,
+            availabilityZone: azs.then(azs => azs.names?.[0]),
+            cidrBlock: SubnetCidrBlocks.PublicOne,
+            mapPublicIpOnLaunch: true,
+            tags: {
+                Name: `${name}-public-subnet-one`,
+                Type: 'public',
+            }
+        }, {parent: this})
+
+        const publicSubnetTwo = new aws.ec2.Subnet(`${name}-public-subnet-two`, {
+            vpcId: vpc.id,
+            availabilityZone: azs.then(azs => azs.names?.[1]),
+            cidrBlock: SubnetCidrBlocks.PublicTwo,
+            mapPublicIpOnLaunch: true,
+            tags: {
+                Name: `${name}-public-subnet-two`,
+                Type: 'public',
+            }
+        }, {parent: this})
+
+                // Setup networking resources for the public subnets.
+        const internetGateway= new aws.ec2.InternetGateway(`${name}-internet-gateway`, {
+            vpcId: vpc.id,
+            tags: {
+                Name: `${name}-internet-gateway`
+            }
+        }, {parent: this})
+
+        const publicRouteTable = new aws.ec2.RouteTable(`${name}-public-route-table`, {
+            vpcId: vpc.id,
+            tags: {
+                Name: `${name}-public-route-table`
+            }
+        }, {parent: this})
+
+        new aws.ec2.Route(`${name}-public-route`, {
+            routeTableId: publicRouteTable.id,
+            destinationCidrBlock: "0.0.0.0/0",
+            gatewayId: internetGateway.id
+        }, {parent: this})
+
+        new aws.ec2.RouteTableAssociation(`${name}-public-subnet-one-rta`, {
+            subnetId: publicSubnetOne.id,
+            routeTableId: publicRouteTable.id
+        }, {parent: this})
+        new aws.ec2.RouteTableAssociation(`${name}-public-subnet-two-rta`, {
+            subnetId: publicSubnetTwo.id,
+            routeTableId: publicRouteTable.id,
+        }, {parent: this})
+
+    }
+}
diff --git a/ops/index.ts b/ops/index.ts
new file mode 100644
index 0000000..a5c0a7a
--- /dev/null
+++ b/ops/index.ts
@@ -0,0 +1,103 @@
+import * as pulumi from "@pulumi/pulumi";
+import {Vpc} from "./components/vpc";
+import {DocumentDBCluster} from "./components/docdb";
+import {Crawler} from "./components/crawler";
+import {MessageQueueBroker} from "./components/mq";
+import {Service} from "./components/ecs/service";
+import {ECSCluster} from "./components/ecs/cluster";
+
+const vpc= new Vpc("network-overlay", {})
+
+const docdb = new DocumentDBCluster("warehouse", {
+    vpcId: vpc.id,
+    instanceClass: "db.t3.medium",
+}, {dependsOn: vpc})
+
+const lambda = new Crawler("crawler", {
+    vpcId: vpc.id,
+    timeout: 900,
+    memory: 3008
+}, {dependsOn: docdb})
+
+const mq = new MessageQueueBroker("mq", {
+    vpcId: vpc.id,
+}, {dependsOn: lambda})
+
+const cluster = new ECSCluster("streaming", {
+    vpcId: vpc.id
+})
+
+const bytewaxWorker = new Service("bytewax-worker", {
+    vpcId: vpc.id,
+    cluster: cluster.name,
+    containerPort: 9000,
+    secrets: [
+        {
+            name: "MONGO_DATABASE_HOST",
+            parameter: "database/host",
+        },
+        {
+            name: "OPENAI_API_KEY",
+            parameter: "database/host",
+        },
+        {
+            name: "QDRANT_DATABASE_HOST",
+            parameter: "database/username",
+        },
+        {
+            name: "QDRANT_DATABASE_PORT",
+            parameter: "database/host",
+        },
+        {
+            name: "QDRANT_APIKEY",
+            parameter: "database/username",
+        },
+        {
+            name: "RABBITMQ_HOST",
+            parameter: "database/host",
+        },
+        {
+            name: "RABBITMQ_PORT",
+            parameter: "database/username",
+        },
+        {
+            name: "RABBITMQ_DEFAULT_USERNAME",
+            parameter: "database/host",
+        },
+        {
+            name: "RABBITMQ_DEFAULT_PASSWORD",
+            parameter: "database/username",
+        },
+    ]
+})
+
+const cdc = new Service("cdc", {
+    vpcId: vpc.id,
+    cluster: cluster.name,
+    containerPort: 9000,
+    secrets: [
+        {
+            name: "MONGO_DATABASE_HOST",
+            parameter: "database/host",
+        },
+        {
+            name: "RABBITMQ_HOST",
+            parameter: "database/host",
+        },
+        {
+            name: "RABBITMQ_PORT",
+            parameter: "database/username",
+        },
+        {
+            name: "RABBITMQ_DEFAULT_USERNAME",
+            parameter: "database/host",
+        },
+        {
+            name: "RABBITMQ_DEFAULT_PASSWORD",
+            parameter: "database/username",
+        },
+    ]
+})
+
+
+export const VpcID: pulumi.Output<string> = vpc.id
diff --git a/ops/package-lock.json b/ops/package-lock.json
new file mode 100644
index 0000000..8bbeb7c
--- /dev/null
+++ b/ops/package-lock.json
@@ -0,0 +1,3340 @@
+{
+    "name": "ops",
+    "lockfileVersion": 3,
+    "requires": true,
+    "packages": {
+        "": {
+            "name": "ops",
+            "dependencies": {
+                "@pulumi/aws": "^6.25.1",
+                "@pulumi/pulumi": "^3.0.0"
+            },
+            "devDependencies": {
+                "@types/node": "^18"
+            }
+        },
+        "node_modules/@grpc/grpc-js": {
+            "version": "1.10.3",
+            "resolved": "https://registry.npmjs.org/@grpc/grpc-js/-/grpc-js-1.10.3.tgz",
+            "integrity": "sha512-qiO9MNgYnwbvZ8MK0YLWbnGrNX3zTcj6/Ef7UHu5ZofER3e2nF3Y35GaPo9qNJJ/UJQKa4KL+z/F4Q8Q+uCdUQ==",
+            "dependencies": {
+                "@grpc/proto-loader": "^0.7.10",
+                "@js-sdsl/ordered-map": "^4.4.2"
+            },
+            "engines": {
+                "node": ">=12.10.0"
+            }
+        },
+        "node_modules/@grpc/proto-loader": {
+            "version": "0.7.10",
+            "resolved": "https://registry.npmjs.org/@grpc/proto-loader/-/proto-loader-0.7.10.tgz",
+            "integrity": "sha512-CAqDfoaQ8ykFd9zqBDn4k6iWT9loLAlc2ETmDFS9JCD70gDcnA4L3AFEo2iV7KyAtAAHFW9ftq1Fz+Vsgq80RQ==",
+            "dependencies": {
+                "lodash.camelcase": "^4.3.0",
+                "long": "^5.0.0",
+                "protobufjs": "^7.2.4",
+                "yargs": "^17.7.2"
+            },
+            "bin": {
+                "proto-loader-gen-types": "build/bin/proto-loader-gen-types.js"
+            },
+            "engines": {
+                "node": ">=6"
+            }
+        },
+        "node_modules/@isaacs/cliui": {
+            "version": "8.0.2",
+            "resolved": "https://registry.npmjs.org/@isaacs/cliui/-/cliui-8.0.2.tgz",
+            "integrity": "sha512-O8jcjabXaleOG9DQ0+ARXWZBTfnP4WNAqzuiJK7ll44AmxGKv/J2M4TPjxjY3znBCfvBXFzucm1twdyFybFqEA==",
+            "dependencies": {
+                "string-width": "^5.1.2",
+                "string-width-cjs": "npm:string-width@^4.2.0",
+                "strip-ansi": "^7.0.1",
+                "strip-ansi-cjs": "npm:strip-ansi@^6.0.1",
+                "wrap-ansi": "^8.1.0",
+                "wrap-ansi-cjs": "npm:wrap-ansi@^7.0.0"
+            },
+            "engines": {
+                "node": ">=12"
+            }
+        },
+        "node_modules/@isaacs/string-locale-compare": {
+            "version": "1.1.0",
+            "resolved": "https://registry.npmjs.org/@isaacs/string-locale-compare/-/string-locale-compare-1.1.0.tgz",
+            "integrity": "sha512-SQ7Kzhh9+D+ZW9MA0zkYv3VXhIDNx+LzM6EJ+/65I3QY+enU6Itte7E5XX7EWrqLW2FN4n06GWzBnPoC3th2aQ=="
+        },
+        "node_modules/@js-sdsl/ordered-map": {
+            "version": "4.4.2",
+            "resolved": "https://registry.npmjs.org/@js-sdsl/ordered-map/-/ordered-map-4.4.2.tgz",
+            "integrity": "sha512-iUKgm52T8HOE/makSxjqoWhe95ZJA1/G1sYsGev2JDKUSS14KAgg1LHb+Ba+IPow0xflbnSkOsZcO08C7w1gYw==",
+            "funding": {
+                "type": "opencollective",
+                "url": "https://opencollective.com/js-sdsl"
+            }
+        },
+        "node_modules/@logdna/tail-file": {
+            "version": "2.2.0",
+            "resolved": "https://registry.npmjs.org/@logdna/tail-file/-/tail-file-2.2.0.tgz",
+            "integrity": "sha512-XGSsWDweP80Fks16lwkAUIr54ICyBs6PsI4mpfTLQaWgEJRtY9xEV+PeyDpJ+sJEGZxqINlpmAwe/6tS1pP8Ng==",
+            "engines": {
+                "node": ">=10.3.0"
+            }
+        },
+        "node_modules/@npmcli/agent": {
+            "version": "2.2.1",
+            "resolved": "https://registry.npmjs.org/@npmcli/agent/-/agent-2.2.1.tgz",
+            "integrity": "sha512-H4FrOVtNyWC8MUwL3UfjOsAihHvT1Pe8POj3JvjXhSTJipsZMtgUALCT4mGyYZNxymkUfOw3PUj6dE4QPp6osQ==",
+            "dependencies": {
+                "agent-base": "^7.1.0",
+                "http-proxy-agent": "^7.0.0",
+                "https-proxy-agent": "^7.0.1",
+                "lru-cache": "^10.0.1",
+                "socks-proxy-agent": "^8.0.1"
+            },
+            "engines": {
+                "node": "^16.14.0 || >=18.0.0"
+            }
+        },
+        "node_modules/@npmcli/arborist": {
+            "version": "7.4.0",
+            "resolved": "https://registry.npmjs.org/@npmcli/arborist/-/arborist-7.4.0.tgz",
+            "integrity": "sha512-VFsUaTrV8NR+0E2I+xhp6pPC5eAbMmSMSMZbS57aogLc6du6HWBPATFOaiNWwp1QTFVeP4aLhYixQM9hHfaAsA==",
+            "dependencies": {
+                "@isaacs/string-locale-compare": "^1.1.0",
+                "@npmcli/fs": "^3.1.0",
+                "@npmcli/installed-package-contents": "^2.0.2",
+                "@npmcli/map-workspaces": "^3.0.2",
+                "@npmcli/metavuln-calculator": "^7.0.0",
+                "@npmcli/name-from-folder": "^2.0.0",
+                "@npmcli/node-gyp": "^3.0.0",
+                "@npmcli/package-json": "^5.0.0",
+                "@npmcli/query": "^3.1.0",
+                "@npmcli/run-script": "^7.0.2",
+                "bin-links": "^4.0.1",
+                "cacache": "^18.0.0",
+                "common-ancestor-path": "^1.0.1",
+                "hosted-git-info": "^7.0.1",
+                "json-parse-even-better-errors": "^3.0.0",
+                "json-stringify-nice": "^1.1.4",
+                "minimatch": "^9.0.0",
+                "nopt": "^7.0.0",
+                "npm-install-checks": "^6.2.0",
+                "npm-package-arg": "^11.0.1",
+                "npm-pick-manifest": "^9.0.0",
+                "npm-registry-fetch": "^16.0.0",
+                "npmlog": "^7.0.1",
+                "pacote": "^17.0.4",
+                "parse-conflict-json": "^3.0.0",
+                "proc-log": "^3.0.0",
+                "promise-all-reject-late": "^1.0.0",
+                "promise-call-limit": "^3.0.1",
+                "read-package-json-fast": "^3.0.2",
+                "semver": "^7.3.7",
+                "ssri": "^10.0.5",
+                "treeverse": "^3.0.0",
+                "walk-up-path": "^3.0.1"
+            },
+            "bin": {
+                "arborist": "bin/index.js"
+            },
+            "engines": {
+                "node": "^16.14.0 || >=18.0.0"
+            }
+        },
+        "node_modules/@npmcli/fs": {
+            "version": "3.1.0",
+            "resolved": "https://registry.npmjs.org/@npmcli/fs/-/fs-3.1.0.tgz",
+            "integrity": "sha512-7kZUAaLscfgbwBQRbvdMYaZOWyMEcPTH/tJjnyAWJ/dvvs9Ef+CERx/qJb9GExJpl1qipaDGn7KqHnFGGixd0w==",
+            "dependencies": {
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diff --git a/ops/package.json b/ops/package.json
new file mode 100644
index 0000000..2bcd77b
--- /dev/null
+++ b/ops/package.json
@@ -0,0 +1,12 @@
+{
+    "name": "decodingml",
+    "main": "index.ts",
+    "devDependencies": {
+        "@types/node": "^18"
+    },
+    "dependencies": {
+        "@pulumi/pulumi": "^3.0.0",
+        "@pulumi/aws": "^6.0.0",
+        "@pulumi/awsx": "^2.0.2"
+    }
+}
diff --git a/ops/tsconfig.json b/ops/tsconfig.json
new file mode 100644
index 0000000..ab65afa
--- /dev/null
+++ b/ops/tsconfig.json
@@ -0,0 +1,18 @@
+{
+    "compilerOptions": {
+        "strict": true,
+        "outDir": "bin",
+        "target": "es2016",
+        "module": "commonjs",
+        "moduleResolution": "node",
+        "sourceMap": true,
+        "experimentalDecorators": true,
+        "pretty": true,
+        "noFallthroughCasesInSwitch": true,
+        "noImplicitReturns": true,
+        "forceConsistentCasingInFileNames": true
+    },
+    "files": [
+        "index.ts"
+    ]
+}
diff --git a/ops/yarn.lock b/ops/yarn.lock
new file mode 100644
index 0000000..98581c2
--- /dev/null
+++ b/ops/yarn.lock
@@ -0,0 +1,3231 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+"@aws-crypto/crc32@3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/crc32/-/crc32-3.0.0.tgz#07300eca214409c33e3ff769cd5697b57fdd38fa"
+  integrity sha512-IzSgsrxUcsrejQbPVilIKy16kAT52EwB6zSaI+M3xxIhKh5+aldEyvI+z6erM7TCLB2BJsFrtHjp6/4/sr+3dA==
+  dependencies:
+    "@aws-crypto/util" "^3.0.0"
+    "@aws-sdk/types" "^3.222.0"
+    tslib "^1.11.1"
+
+"@aws-crypto/ie11-detection@^3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/ie11-detection/-/ie11-detection-3.0.0.tgz#640ae66b4ec3395cee6a8e94ebcd9f80c24cd688"
+  integrity sha512-341lBBkiY1DfDNKai/wXM3aujNBkXR7tq1URPQDL9wi3AUbI80NR74uF1TXHMm7po1AcnFk8iu2S2IeU/+/A+Q==
+  dependencies:
+    tslib "^1.11.1"
+
+"@aws-crypto/sha256-browser@3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/sha256-browser/-/sha256-browser-3.0.0.tgz#05f160138ab893f1c6ba5be57cfd108f05827766"
+  integrity sha512-8VLmW2B+gjFbU5uMeqtQM6Nj0/F1bro80xQXCW6CQBWgosFWXTx77aeOF5CAIAmbOK64SdMBJdNr6J41yP5mvQ==
+  dependencies:
+    "@aws-crypto/ie11-detection" "^3.0.0"
+    "@aws-crypto/sha256-js" "^3.0.0"
+    "@aws-crypto/supports-web-crypto" "^3.0.0"
+    "@aws-crypto/util" "^3.0.0"
+    "@aws-sdk/types" "^3.222.0"
+    "@aws-sdk/util-locate-window" "^3.0.0"
+    "@aws-sdk/util-utf8-browser" "^3.0.0"
+    tslib "^1.11.1"
+
+"@aws-crypto/sha256-js@3.0.0", "@aws-crypto/sha256-js@^3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/sha256-js/-/sha256-js-3.0.0.tgz#f06b84d550d25521e60d2a0e2a90139341e007c2"
+  integrity sha512-PnNN7os0+yd1XvXAy23CFOmTbMaDxgxXtTKHybrJ39Y8kGzBATgBFibWJKH6BhytLI/Zyszs87xCOBNyBig6vQ==
+  dependencies:
+    "@aws-crypto/util" "^3.0.0"
+    "@aws-sdk/types" "^3.222.0"
+    tslib "^1.11.1"
+
+"@aws-crypto/supports-web-crypto@^3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/supports-web-crypto/-/supports-web-crypto-3.0.0.tgz#5d1bf825afa8072af2717c3e455f35cda0103ec2"
+  integrity sha512-06hBdMwUAb2WFTuGG73LSC0wfPu93xWwo5vL2et9eymgmu3Id5vFAHBbajVWiGhPO37qcsdCap/FqXvJGJWPIg==
+  dependencies:
+    tslib "^1.11.1"
+
+"@aws-crypto/util@^3.0.0":
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/@aws-crypto/util/-/util-3.0.0.tgz#1c7ca90c29293f0883468ad48117937f0fe5bfb0"
+  integrity sha512-2OJlpeJpCR48CC8r+uKVChzs9Iungj9wkZrl8Z041DWEWvyIHILYKCPNzJghKsivj+S3mLo6BVc7mBNzdxA46w==
+  dependencies:
+    "@aws-sdk/types" "^3.222.0"
+    "@aws-sdk/util-utf8-browser" "^3.0.0"
+    tslib "^1.11.1"
+
+"@aws-sdk/client-ecs@^3.405.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/client-ecs/-/client-ecs-3.535.0.tgz#4246be7569e9be73bd10482f2cfd21794ed5817e"
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+  dependencies:
+    "@aws-crypto/sha256-browser" "3.0.0"
+    "@aws-crypto/sha256-js" "3.0.0"
+    "@aws-sdk/client-sts" "3.535.0"
+    "@aws-sdk/core" "3.535.0"
+    "@aws-sdk/credential-provider-node" "3.535.0"
+    "@aws-sdk/middleware-host-header" "3.535.0"
+    "@aws-sdk/middleware-logger" "3.535.0"
+    "@aws-sdk/middleware-recursion-detection" "3.535.0"
+    "@aws-sdk/middleware-user-agent" "3.535.0"
+    "@aws-sdk/region-config-resolver" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
+    "@aws-sdk/util-endpoints" "3.535.0"
+    "@aws-sdk/util-user-agent-browser" "3.535.0"
+    "@aws-sdk/util-user-agent-node" "3.535.0"
+    "@smithy/config-resolver" "^2.2.0"
+    "@smithy/core" "^1.4.0"
+    "@smithy/fetch-http-handler" "^2.5.0"
+    "@smithy/hash-node" "^2.2.0"
+    "@smithy/invalid-dependency" "^2.2.0"
+    "@smithy/middleware-content-length" "^2.2.0"
+    "@smithy/middleware-endpoint" "^2.5.0"
+    "@smithy/middleware-retry" "^2.2.0"
+    "@smithy/middleware-serde" "^2.3.0"
+    "@smithy/middleware-stack" "^2.2.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/node-http-handler" "^2.5.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    "@smithy/util-base64" "^2.3.0"
+    "@smithy/util-body-length-browser" "^2.2.0"
+    "@smithy/util-body-length-node" "^2.3.0"
+    "@smithy/util-defaults-mode-browser" "^2.2.0"
+    "@smithy/util-defaults-mode-node" "^2.3.0"
+    "@smithy/util-endpoints" "^1.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-retry" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    "@smithy/util-waiter" "^2.2.0"
+    tslib "^2.6.2"
+    uuid "^9.0.1"
+
+"@aws-sdk/client-sso-oidc@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/client-sso-oidc/-/client-sso-oidc-3.535.0.tgz#64666c2f7bed8510938ba2b481429fea8f97473d"
+  integrity sha512-M2cG4EQXDpAJQyq33ORIr6abmdX9p9zX0ssVy8XwFNB7lrgoIKxuVoGL+fX+XMgecl24x7ELz6b4QlILOevbCw==
+  dependencies:
+    "@aws-crypto/sha256-browser" "3.0.0"
+    "@aws-crypto/sha256-js" "3.0.0"
+    "@aws-sdk/client-sts" "3.535.0"
+    "@aws-sdk/core" "3.535.0"
+    "@aws-sdk/middleware-host-header" "3.535.0"
+    "@aws-sdk/middleware-logger" "3.535.0"
+    "@aws-sdk/middleware-recursion-detection" "3.535.0"
+    "@aws-sdk/middleware-user-agent" "3.535.0"
+    "@aws-sdk/region-config-resolver" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
+    "@aws-sdk/util-endpoints" "3.535.0"
+    "@aws-sdk/util-user-agent-browser" "3.535.0"
+    "@aws-sdk/util-user-agent-node" "3.535.0"
+    "@smithy/config-resolver" "^2.2.0"
+    "@smithy/core" "^1.4.0"
+    "@smithy/fetch-http-handler" "^2.5.0"
+    "@smithy/hash-node" "^2.2.0"
+    "@smithy/invalid-dependency" "^2.2.0"
+    "@smithy/middleware-content-length" "^2.2.0"
+    "@smithy/middleware-endpoint" "^2.5.0"
+    "@smithy/middleware-retry" "^2.2.0"
+    "@smithy/middleware-serde" "^2.3.0"
+    "@smithy/middleware-stack" "^2.2.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/node-http-handler" "^2.5.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    "@smithy/util-base64" "^2.3.0"
+    "@smithy/util-body-length-browser" "^2.2.0"
+    "@smithy/util-body-length-node" "^2.3.0"
+    "@smithy/util-defaults-mode-browser" "^2.2.0"
+    "@smithy/util-defaults-mode-node" "^2.3.0"
+    "@smithy/util-endpoints" "^1.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-retry" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@aws-sdk/client-sso@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/client-sso/-/client-sso-3.535.0.tgz#c405aaf880cb695aa2f5070a8827955274fc9df2"
+  integrity sha512-h9eQRdFnjDRVBnPJIKXuX7D+isSAioIfZPC4PQwsL5BscTRlk4c90DX0R0uk64YUtp7LZu8TNtrosFZ/1HtTrQ==
+  dependencies:
+    "@aws-crypto/sha256-browser" "3.0.0"
+    "@aws-crypto/sha256-js" "3.0.0"
+    "@aws-sdk/core" "3.535.0"
+    "@aws-sdk/middleware-host-header" "3.535.0"
+    "@aws-sdk/middleware-logger" "3.535.0"
+    "@aws-sdk/middleware-recursion-detection" "3.535.0"
+    "@aws-sdk/middleware-user-agent" "3.535.0"
+    "@aws-sdk/region-config-resolver" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
+    "@aws-sdk/util-endpoints" "3.535.0"
+    "@aws-sdk/util-user-agent-browser" "3.535.0"
+    "@aws-sdk/util-user-agent-node" "3.535.0"
+    "@smithy/config-resolver" "^2.2.0"
+    "@smithy/core" "^1.4.0"
+    "@smithy/fetch-http-handler" "^2.5.0"
+    "@smithy/hash-node" "^2.2.0"
+    "@smithy/invalid-dependency" "^2.2.0"
+    "@smithy/middleware-content-length" "^2.2.0"
+    "@smithy/middleware-endpoint" "^2.5.0"
+    "@smithy/middleware-retry" "^2.2.0"
+    "@smithy/middleware-serde" "^2.3.0"
+    "@smithy/middleware-stack" "^2.2.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/node-http-handler" "^2.5.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    "@smithy/util-base64" "^2.3.0"
+    "@smithy/util-body-length-browser" "^2.2.0"
+    "@smithy/util-body-length-node" "^2.3.0"
+    "@smithy/util-defaults-mode-browser" "^2.2.0"
+    "@smithy/util-defaults-mode-node" "^2.3.0"
+    "@smithy/util-endpoints" "^1.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-retry" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@aws-sdk/client-sts@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/client-sts/-/client-sts-3.535.0.tgz#0f518fe338c6b7a8b8a897e2ccee65d06dc0040f"
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+  dependencies:
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+    "@aws-crypto/sha256-js" "3.0.0"
+    "@aws-sdk/core" "3.535.0"
+    "@aws-sdk/middleware-host-header" "3.535.0"
+    "@aws-sdk/middleware-logger" "3.535.0"
+    "@aws-sdk/middleware-recursion-detection" "3.535.0"
+    "@aws-sdk/middleware-user-agent" "3.535.0"
+    "@aws-sdk/region-config-resolver" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
+    "@aws-sdk/util-endpoints" "3.535.0"
+    "@aws-sdk/util-user-agent-browser" "3.535.0"
+    "@aws-sdk/util-user-agent-node" "3.535.0"
+    "@smithy/config-resolver" "^2.2.0"
+    "@smithy/core" "^1.4.0"
+    "@smithy/fetch-http-handler" "^2.5.0"
+    "@smithy/hash-node" "^2.2.0"
+    "@smithy/invalid-dependency" "^2.2.0"
+    "@smithy/middleware-content-length" "^2.2.0"
+    "@smithy/middleware-endpoint" "^2.5.0"
+    "@smithy/middleware-retry" "^2.2.0"
+    "@smithy/middleware-serde" "^2.3.0"
+    "@smithy/middleware-stack" "^2.2.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/node-http-handler" "^2.5.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    "@smithy/util-base64" "^2.3.0"
+    "@smithy/util-body-length-browser" "^2.2.0"
+    "@smithy/util-body-length-node" "^2.3.0"
+    "@smithy/util-defaults-mode-browser" "^2.2.0"
+    "@smithy/util-defaults-mode-node" "^2.3.0"
+    "@smithy/util-endpoints" "^1.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-retry" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@aws-sdk/core@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/core/-/core-3.535.0.tgz#f3a726c297cea9634d19a1db4e958c918c506c8b"
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+  dependencies:
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+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/signature-v4" "^2.2.0"
+    "@smithy/smithy-client" "^2.5.0"
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+    fast-xml-parser "4.2.5"
+    tslib "^2.6.2"
+
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+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-env/-/credential-provider-env-3.535.0.tgz#26248e263a8107953d5496cb3760d4e7c877abcf"
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+    tslib "^2.6.2"
+
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+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-http/-/credential-provider-http-3.535.0.tgz#0a42f6b1a61d927bbce9f4afd25112f486bd05da"
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+  dependencies:
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+    tslib "^2.6.2"
+
+"@aws-sdk/credential-provider-ini@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-ini/-/credential-provider-ini-3.535.0.tgz#b121b1aba2916e3f45745cd690b4082421a7c286"
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+  dependencies:
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+    "@aws-sdk/credential-provider-env" "3.535.0"
+    "@aws-sdk/credential-provider-process" "3.535.0"
+    "@aws-sdk/credential-provider-sso" "3.535.0"
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+    tslib "^2.6.2"
+
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+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-node/-/credential-provider-node-3.535.0.tgz#6739b4b52a9cce29dc8e70c9a7290b89cdc4b904"
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+  dependencies:
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+    "@aws-sdk/credential-provider-ini" "3.535.0"
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+    "@aws-sdk/credential-provider-web-identity" "3.535.0"
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+
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+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-process/-/credential-provider-process-3.535.0.tgz#ea1e8a38a32e36bbdc3f75eb03352e6eafa0c659"
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+  dependencies:
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+    tslib "^2.6.2"
+
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+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-sso/-/credential-provider-sso-3.535.0.tgz#dfc7c2f39f9ca965becd7e5b9414cd1bb2217490"
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+  dependencies:
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+    "@aws-sdk/token-providers" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
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+    tslib "^2.6.2"
+
+"@aws-sdk/credential-provider-web-identity@3.535.0":
+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/credential-provider-web-identity/-/credential-provider-web-identity-3.535.0.tgz#f1d3a72ff958cbd7e052c5109755379745ac35e0"
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+  dependencies:
+    "@aws-sdk/client-sts" "3.535.0"
+    "@aws-sdk/types" "3.535.0"
+    "@smithy/property-provider" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
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+  version "3.535.0"
+  resolved "https://registry.yarnpkg.com/@aws-sdk/middleware-host-header/-/middleware-host-header-3.535.0.tgz#d5264f813592f5e77df25e5a14bbb0e6441812db"
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+  dependencies:
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+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/types" "^2.12.0"
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+  resolved "https://registry.yarnpkg.com/@smithy/credential-provider-imds/-/credential-provider-imds-2.3.0.tgz#326ce401b82e53f3c7ee4862a066136959a06166"
+  integrity sha512-BWB9mIukO1wjEOo1Ojgl6LrG4avcaC7T/ZP6ptmAaW4xluhSIPZhY+/PI5YKzlk+jsm+4sQZB45Bt1OfMeQa3w==
+  dependencies:
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/property-provider" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/eventstream-codec@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/eventstream-codec/-/eventstream-codec-2.2.0.tgz#63d74fa817188995eb55e792a38060b0ede98dc4"
+  integrity sha512-8janZoJw85nJmQZc4L8TuePp2pk1nxLgkxIR0TUjKJ5Dkj5oelB9WtiSSGXCQvNsJl0VSTvK/2ueMXxvpa9GVw==
+  dependencies:
+    "@aws-crypto/crc32" "3.0.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-hex-encoding" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/fetch-http-handler@^2.5.0":
+  version "2.5.0"
+  resolved "https://registry.yarnpkg.com/@smithy/fetch-http-handler/-/fetch-http-handler-2.5.0.tgz#0b8e1562807fdf91fe7dd5cde620d7a03ddc10ac"
+  integrity sha512-BOWEBeppWhLn/no/JxUL/ghTfANTjT7kg3Ww2rPqTUY9R4yHPXxJ9JhMe3Z03LN3aPwiwlpDIUcVw1xDyHqEhw==
+  dependencies:
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/querystring-builder" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-base64" "^2.3.0"
+    tslib "^2.6.2"
+
+"@smithy/hash-node@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/hash-node/-/hash-node-2.2.0.tgz#df29e1e64811be905cb3577703b0e2d0b07fc5cc"
+  integrity sha512-zLWaC/5aWpMrHKpoDF6nqpNtBhlAYKF/7+9yMN7GpdR8CzohnWfGtMznPybnwSS8saaXBMxIGwJqR4HmRp6b3g==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-buffer-from" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@smithy/invalid-dependency@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/invalid-dependency/-/invalid-dependency-2.2.0.tgz#ee3d8980022cb5edb514ac187d159b3e773640f0"
+  integrity sha512-nEDASdbKFKPXN2O6lOlTgrEEOO9NHIeO+HVvZnkqc8h5U9g3BIhWsvzFo+UcUbliMHvKNPD/zVxDrkP1Sbgp8Q==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/is-array-buffer@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/is-array-buffer/-/is-array-buffer-2.2.0.tgz#f84f0d9f9a36601a9ca9381688bd1b726fd39111"
+  integrity sha512-GGP3O9QFD24uGeAXYUjwSTXARoqpZykHadOmA8G5vfJPK0/DC67qa//0qvqrJzL1xc8WQWX7/yc7fwudjPHPhA==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/middleware-content-length@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/middleware-content-length/-/middleware-content-length-2.2.0.tgz#a82e97bd83d8deab69e07fea4512563bedb9461a"
+  integrity sha512-5bl2LG1Ah/7E5cMSC+q+h3IpVHMeOkG0yLRyQT1p2aMJkSrZG7RlXHPuAgb7EyaFeidKEnnd/fNaLLaKlHGzDQ==
+  dependencies:
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/middleware-endpoint@^2.5.0":
+  version "2.5.0"
+  resolved "https://registry.yarnpkg.com/@smithy/middleware-endpoint/-/middleware-endpoint-2.5.0.tgz#9f1459e9b4cbf00fadfd99e98f88d4b1a2aeb987"
+  integrity sha512-OBhI9ZEAG8Xen0xsFJwwNOt44WE2CWkfYIxTognC8x42Lfsdf0VN/wCMqpdkySMDio/vts10BiovAxQp0T0faA==
+  dependencies:
+    "@smithy/middleware-serde" "^2.3.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/shared-ini-file-loader" "^2.4.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/url-parser" "^2.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/middleware-retry@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/middleware-retry/-/middleware-retry-2.2.0.tgz#ff48ac01ad57394eeea15a0146a86079cf6364b7"
+  integrity sha512-PsjDOLpbevgn37yJbawmfVoanru40qVA8UEf2+YA1lvOefmhuhL6ZbKtGsLAWDRnE1OlAmedsbA/htH6iSZjNA==
+  dependencies:
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/service-error-classification" "^2.1.5"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-retry" "^2.2.0"
+    tslib "^2.6.2"
+    uuid "^8.3.2"
+
+"@smithy/middleware-serde@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/middleware-serde/-/middleware-serde-2.3.0.tgz#a7615ba646a88b6f695f2d55de13d8158181dd13"
+  integrity sha512-sIADe7ojwqTyvEQBe1nc/GXB9wdHhi9UwyX0lTyttmUWDJLP655ZYE1WngnNyXREme8I27KCaUhyhZWRXL0q7Q==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/middleware-stack@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/middleware-stack/-/middleware-stack-2.2.0.tgz#3fb49eae6313f16f6f30fdaf28e11a7321f34d9f"
+  integrity sha512-Qntc3jrtwwrsAC+X8wms8zhrTr0sFXnyEGhZd9sLtsJ/6gGQKFzNB+wWbOcpJd7BR8ThNCoKt76BuQahfMvpeA==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/node-config-provider@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/node-config-provider/-/node-config-provider-2.3.0.tgz#9fac0c94a14c5b5b8b8fa37f20c310a844ab9922"
+  integrity sha512-0elK5/03a1JPWMDPaS726Iw6LpQg80gFut1tNpPfxFuChEEklo2yL823V94SpTZTxmKlXFtFgsP55uh3dErnIg==
+  dependencies:
+    "@smithy/property-provider" "^2.2.0"
+    "@smithy/shared-ini-file-loader" "^2.4.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/node-http-handler@^2.5.0":
+  version "2.5.0"
+  resolved "https://registry.yarnpkg.com/@smithy/node-http-handler/-/node-http-handler-2.5.0.tgz#7b5e0565dd23d340380489bd5fe4316d2bed32de"
+  integrity sha512-mVGyPBzkkGQsPoxQUbxlEfRjrj6FPyA3u3u2VXGr9hT8wilsoQdZdvKpMBFMB8Crfhv5dNkKHIW0Yyuc7eABqA==
+  dependencies:
+    "@smithy/abort-controller" "^2.2.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/querystring-builder" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/property-provider@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/property-provider/-/property-provider-2.2.0.tgz#37e3525a3fa3e11749f86a4f89f0fd7765a6edb0"
+  integrity sha512-+xiil2lFhtTRzXkx8F053AV46QnIw6e7MV8od5Mi68E1ICOjCeCHw2XfLnDEUHnT9WGUIkwcqavXjfwuJbGlpg==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/protocol-http@^3.3.0":
+  version "3.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/protocol-http/-/protocol-http-3.3.0.tgz#a37df7b4bb4960cdda560ce49acfd64c455e4090"
+  integrity sha512-Xy5XK1AFWW2nlY/biWZXu6/krgbaf2dg0q492D8M5qthsnU2H+UgFeZLbM76FnH7s6RO/xhQRkj+T6KBO3JzgQ==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/querystring-builder@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/querystring-builder/-/querystring-builder-2.2.0.tgz#22937e19fcd0aaa1a3e614ef8cb6f8e86756a4ef"
+  integrity sha512-L1kSeviUWL+emq3CUVSgdogoM/D9QMFaqxL/dd0X7PCNWmPXqt+ExtrBjqT0V7HLN03Vs9SuiLrG3zy3JGnE5A==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-uri-escape" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/querystring-parser@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/querystring-parser/-/querystring-parser-2.2.0.tgz#24a5633f4b3806ff2888d4c2f4169e105fdffd79"
+  integrity sha512-BvHCDrKfbG5Yhbpj4vsbuPV2GgcpHiAkLeIlcA1LtfpMz3jrqizP1+OguSNSj1MwBHEiN+jwNisXLGdajGDQJA==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/service-error-classification@^2.1.5":
+  version "2.1.5"
+  resolved "https://registry.yarnpkg.com/@smithy/service-error-classification/-/service-error-classification-2.1.5.tgz#0568a977cc0db36299d8703a5d8609c1f600c005"
+  integrity sha512-uBDTIBBEdAQryvHdc5W8sS5YX7RQzF683XrHePVdFmAgKiMofU15FLSM0/HU03hKTnazdNRFa0YHS7+ArwoUSQ==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+
+"@smithy/shared-ini-file-loader@^2.4.0":
+  version "2.4.0"
+  resolved "https://registry.yarnpkg.com/@smithy/shared-ini-file-loader/-/shared-ini-file-loader-2.4.0.tgz#1636d6eb9bff41e36ac9c60364a37fd2ffcb9947"
+  integrity sha512-WyujUJL8e1B6Z4PBfAqC/aGY1+C7T0w20Gih3yrvJSk97gpiVfB+y7c46T4Nunk+ZngLq0rOIdeVeIklk0R3OA==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/signature-v4@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/signature-v4/-/signature-v4-2.2.0.tgz#8fe6a574188b71fba6056111b88d50c84babb060"
+  integrity sha512-+B5TNzj/fRZzVW3z8UUJOkNx15+4E0CLuvJmJUA1JUIZFp3rdJ/M2H5r2SqltaVPXL0oIxv/6YK92T9TsFGbFg==
+  dependencies:
+    "@smithy/eventstream-codec" "^2.2.0"
+    "@smithy/is-array-buffer" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-hex-encoding" "^2.2.0"
+    "@smithy/util-middleware" "^2.2.0"
+    "@smithy/util-uri-escape" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@smithy/smithy-client@^2.5.0":
+  version "2.5.0"
+  resolved "https://registry.yarnpkg.com/@smithy/smithy-client/-/smithy-client-2.5.0.tgz#8de4fff221d232dda34a8e706d6a4f2911dffe2e"
+  integrity sha512-DDXWHWdimtS3y/Kw1Jo46KQ0ZYsDKcldFynQERUGBPDpkW1lXOTHy491ALHjwfiBQvzsVKVxl5+ocXNIgJuX4g==
+  dependencies:
+    "@smithy/middleware-endpoint" "^2.5.0"
+    "@smithy/middleware-stack" "^2.2.0"
+    "@smithy/protocol-http" "^3.3.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-stream" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/types@^2.12.0":
+  version "2.12.0"
+  resolved "https://registry.yarnpkg.com/@smithy/types/-/types-2.12.0.tgz#c44845f8ba07e5e8c88eda5aed7e6a0c462da041"
+  integrity sha512-QwYgloJ0sVNBeBuBs65cIkTbfzV/Q6ZNPCJ99EICFEdJYG50nGIY/uYXp+TbsdJReIuPr0a0kXmCvren3MbRRw==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/url-parser@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/url-parser/-/url-parser-2.2.0.tgz#6fcda6116391a4f61fef5580eb540e128359b3c0"
+  integrity sha512-hoA4zm61q1mNTpksiSWp2nEl1dt3j726HdRhiNgVJQMj7mLp7dprtF57mOB6JvEk/x9d2bsuL5hlqZbBuHQylQ==
+  dependencies:
+    "@smithy/querystring-parser" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/util-base64@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-base64/-/util-base64-2.3.0.tgz#312dbb4d73fb94249c7261aee52de4195c2dd8e2"
+  integrity sha512-s3+eVwNeJuXUwuMbusncZNViuhv2LjVJ1nMwTqSA0XAC7gjKhqqxRdJPhR8+YrkoZ9IiIbFk/yK6ACe/xlF+hw==
+  dependencies:
+    "@smithy/util-buffer-from" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@smithy/util-body-length-browser@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-body-length-browser/-/util-body-length-browser-2.2.0.tgz#25620645c6b62b42594ef4a93b66e6ab70e27d2c"
+  integrity sha512-dtpw9uQP7W+n3vOtx0CfBD5EWd7EPdIdsQnWTDoFf77e3VUf05uA7R7TGipIo8e4WL2kuPdnsr3hMQn9ziYj5w==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/util-body-length-node@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-body-length-node/-/util-body-length-node-2.3.0.tgz#d065a9b5e305ff899536777bbfe075cdc980136f"
+  integrity sha512-ITWT1Wqjubf2CJthb0BuT9+bpzBfXeMokH/AAa5EJQgbv9aPMVfnM76iFIZVFf50hYXGbtiV71BHAthNWd6+dw==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/util-buffer-from@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-buffer-from/-/util-buffer-from-2.2.0.tgz#6fc88585165ec73f8681d426d96de5d402021e4b"
+  integrity sha512-IJdWBbTcMQ6DA0gdNhh/BwrLkDR+ADW5Kr1aZmd4k3DIF6ezMV4R2NIAmT08wQJ3yUK82thHWmC/TnK/wpMMIA==
+  dependencies:
+    "@smithy/is-array-buffer" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/util-config-provider@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-config-provider/-/util-config-provider-2.3.0.tgz#bc79f99562d12a1f8423100ca662a6fb07cde943"
+  integrity sha512-HZkzrRcuFN1k70RLqlNK4FnPXKOpkik1+4JaBoHNJn+RnJGYqaa3c5/+XtLOXhlKzlRgNvyaLieHTW2VwGN0VQ==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/util-defaults-mode-browser@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-defaults-mode-browser/-/util-defaults-mode-browser-2.2.0.tgz#963a9d3c3351272764dd1c5dc07c26f2c8abcb02"
+  integrity sha512-2okTdZaCBvOJszAPU/KSvlimMe35zLOKbQpHhamFJmR7t95HSe0K3C92jQPjKY3PmDBD+7iMkOnuW05F5OlF4g==
+  dependencies:
+    "@smithy/property-provider" "^2.2.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    bowser "^2.11.0"
+    tslib "^2.6.2"
+
+"@smithy/util-defaults-mode-node@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-defaults-mode-node/-/util-defaults-mode-node-2.3.0.tgz#5005058ca0a299f0948b47c288f7c3d4f36cb26e"
+  integrity sha512-hfKXnNLmsW9cmLb/JXKIvtuO6Cf4SuqN5PN1C2Ru/TBIws+m1wSgb+A53vo0r66xzB6E82inKG2J7qtwdi+Kkw==
+  dependencies:
+    "@smithy/config-resolver" "^2.2.0"
+    "@smithy/credential-provider-imds" "^2.3.0"
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/property-provider" "^2.2.0"
+    "@smithy/smithy-client" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/util-endpoints@^1.2.0":
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-endpoints/-/util-endpoints-1.2.0.tgz#b8b805f47e8044c158372f69b88337703117665d"
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+  dependencies:
+    "@smithy/node-config-provider" "^2.3.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/util-hex-encoding@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-hex-encoding/-/util-hex-encoding-2.2.0.tgz#87edb7c88c2f422cfca4bb21f1394ae9602c5085"
+  integrity sha512-7iKXR+/4TpLK194pVjKiasIyqMtTYJsgKgM242Y9uzt5dhHnUDvMNb+3xIhRJ9QhvqGii/5cRUt4fJn3dtXNHQ==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/util-middleware@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-middleware/-/util-middleware-2.2.0.tgz#80cfad40f6cca9ffe42a5899b5cb6abd53a50006"
+  integrity sha512-L1qpleXf9QD6LwLCJ5jddGkgWyuSvWBkJwWAZ6kFkdifdso+sk3L3O1HdmPvCdnCK3IS4qWyPxev01QMnfHSBw==
+  dependencies:
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/util-retry@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-retry/-/util-retry-2.2.0.tgz#e8e019537ab47ba6b2e87e723ec51ee223422d85"
+  integrity sha512-q9+pAFPTfftHXRytmZ7GzLFFrEGavqapFc06XxzZFcSIGERXMerXxCitjOG1prVDR9QdjqotF40SWvbqcCpf8g==
+  dependencies:
+    "@smithy/service-error-classification" "^2.1.5"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@smithy/util-stream@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-stream/-/util-stream-2.2.0.tgz#b1279e417992a0f74afa78d7501658f174ed7370"
+  integrity sha512-17faEXbYWIRst1aU9SvPZyMdWmqIrduZjVOqCPMIsWFNxs5yQQgFrJL6b2SdiCzyW9mJoDjFtgi53xx7EH+BXA==
+  dependencies:
+    "@smithy/fetch-http-handler" "^2.5.0"
+    "@smithy/node-http-handler" "^2.5.0"
+    "@smithy/types" "^2.12.0"
+    "@smithy/util-base64" "^2.3.0"
+    "@smithy/util-buffer-from" "^2.2.0"
+    "@smithy/util-hex-encoding" "^2.2.0"
+    "@smithy/util-utf8" "^2.3.0"
+    tslib "^2.6.2"
+
+"@smithy/util-uri-escape@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-uri-escape/-/util-uri-escape-2.2.0.tgz#56f5764051a33b67bc93fdd2a869f971b0635406"
+  integrity sha512-jtmJMyt1xMD/d8OtbVJ2gFZOSKc+ueYJZPW20ULW1GOp/q/YIM0wNh+u8ZFao9UaIGz4WoPW8hC64qlWLIfoDA==
+  dependencies:
+    tslib "^2.6.2"
+
+"@smithy/util-utf8@^2.3.0":
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-utf8/-/util-utf8-2.3.0.tgz#dd96d7640363259924a214313c3cf16e7dd329c5"
+  integrity sha512-R8Rdn8Hy72KKcebgLiv8jQcQkXoLMOGGv5uI1/k0l+snqkOzQ1R0ChUBCxWMlBsFMekWjq0wRudIweFs7sKT5A==
+  dependencies:
+    "@smithy/util-buffer-from" "^2.2.0"
+    tslib "^2.6.2"
+
+"@smithy/util-waiter@^2.2.0":
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/@smithy/util-waiter/-/util-waiter-2.2.0.tgz#d11baf50637bfaadb9641d6ca1619da413dd2612"
+  integrity sha512-IHk53BVw6MPMi2Gsn+hCng8rFA3ZmR3Rk7GllxDUW9qFJl/hiSvskn7XldkECapQVkIg/1dHpMAxI9xSTaLLSA==
+  dependencies:
+    "@smithy/abort-controller" "^2.2.0"
+    "@smithy/types" "^2.12.0"
+    tslib "^2.6.2"
+
+"@szmarczak/http-timer@^4.0.5":
+  version "4.0.6"
+  resolved "https://registry.yarnpkg.com/@szmarczak/http-timer/-/http-timer-4.0.6.tgz#b4a914bb62e7c272d4e5989fe4440f812ab1d807"
+  integrity sha512-4BAffykYOgO+5nzBWYwE3W90sBgLJoUPRWWcL8wlyiM8IB8ipJz3UMJ9KXQd1RKQXpKp8Tutn80HZtWsu2u76w==
+  dependencies:
+    defer-to-connect "^2.0.0"
+
+"@tufjs/canonical-json@2.0.0":
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/@tufjs/canonical-json/-/canonical-json-2.0.0.tgz#a52f61a3d7374833fca945b2549bc30a2dd40d0a"
+  integrity sha512-yVtV8zsdo8qFHe+/3kw81dSLyF7D576A5cCFCi4X7B39tWT7SekaEFUnvnWJHz+9qO7qJTah1JbrDjWKqFtdWA==
+
+"@tufjs/models@2.0.0":
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/@tufjs/models/-/models-2.0.0.tgz#c7ab241cf11dd29deb213d6817dabb8c99ce0863"
+  integrity sha512-c8nj8BaOExmZKO2DXhDfegyhSGcG9E/mPN3U13L+/PsoWm1uaGiHHjxqSHQiasDBQwDA3aHuw9+9spYAP1qvvg==
+  dependencies:
+    "@tufjs/canonical-json" "2.0.0"
+    minimatch "^9.0.3"
+
+"@types/aws-lambda@^8.10.23":
+  version "8.10.136"
+  resolved "https://registry.yarnpkg.com/@types/aws-lambda/-/aws-lambda-8.10.136.tgz#12a2af86b9123f4e4549992b27e1bf0dcf60d9f9"
+  integrity sha512-cmmgqxdVGhxYK9lZMYYXYRJk6twBo53ivtXjIUEFZxfxe4TkZTZBK3RRWrY2HjJcUIix0mdifn15yjOAat5lTA==
+
+"@types/cacheable-request@^6.0.1":
+  version "6.0.3"
+  resolved "https://registry.yarnpkg.com/@types/cacheable-request/-/cacheable-request-6.0.3.tgz#a430b3260466ca7b5ca5bfd735693b36e7a9d183"
+  integrity sha512-IQ3EbTzGxIigb1I3qPZc1rWJnH0BmSKv5QYTalEwweFvyBDLSAe24zP0le/hyi7ecGfZVlIVAg4BZqb8WBwKqw==
+  dependencies:
+    "@types/http-cache-semantics" "*"
+    "@types/keyv" "^3.1.4"
+    "@types/node" "*"
+    "@types/responselike" "^1.0.0"
+
+"@types/google-protobuf@^3.15.5":
+  version "3.15.12"
+  resolved "https://registry.yarnpkg.com/@types/google-protobuf/-/google-protobuf-3.15.12.tgz#eb2ba0eddd65712211a2b455dc6071d665ccf49b"
+  integrity sha512-40um9QqwHjRS92qnOaDpL7RmDK15NuZYo9HihiJRbYkMQZlWnuH8AdvbMy8/o6lgLmKbDUKa+OALCltHdbOTpQ==
+
+"@types/http-cache-semantics@*":
+  version "4.0.4"
+  resolved "https://registry.yarnpkg.com/@types/http-cache-semantics/-/http-cache-semantics-4.0.4.tgz#b979ebad3919799c979b17c72621c0bc0a31c6c4"
+  integrity sha512-1m0bIFVc7eJWyve9S0RnuRgcQqF/Xd5QsUZAZeQFr1Q3/p9JWoQQEqmVy+DPTNpGXwhgIetAoYF8JSc33q29QA==
+
+"@types/keyv@^3.1.4":
+  version "3.1.4"
+  resolved "https://registry.yarnpkg.com/@types/keyv/-/keyv-3.1.4.tgz#3ccdb1c6751b0c7e52300bcdacd5bcbf8faa75b6"
+  integrity sha512-BQ5aZNSCpj7D6K2ksrRCTmKRLEpnPvWDiLPfoGyhZ++8YtiK9d/3DBKPJgry359X/P1PfruyYwvnvwFjuEiEIg==
+  dependencies:
+    "@types/node" "*"
+
+"@types/node@*", "@types/node@>=13.7.0":
+  version "20.11.30"
+  resolved "https://registry.yarnpkg.com/@types/node/-/node-20.11.30.tgz#9c33467fc23167a347e73834f788f4b9f399d66f"
+  integrity sha512-dHM6ZxwlmuZaRmUPfv1p+KrdD1Dci04FbdEm/9wEMouFqxYoFl5aMkt0VMAUtYRQDyYvD41WJLukhq/ha3YuTw==
+  dependencies:
+    undici-types "~5.26.4"
+
+"@types/node@^18":
+  version "18.19.26"
+  resolved "https://registry.yarnpkg.com/@types/node/-/node-18.19.26.tgz#18991279d0a0e53675285e8cf4a0823766349729"
+  integrity sha512-+wiMJsIwLOYCvUqSdKTrfkS8mpTp+MPINe6+Np4TAGFWWRWiBQ5kSq9nZGCSPkzx9mvT+uEukzpX4MOSCydcvw==
+  dependencies:
+    undici-types "~5.26.4"
+
+"@types/responselike@^1.0.0":
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/@types/responselike/-/responselike-1.0.3.tgz#cc29706f0a397cfe6df89debfe4bf5cea159db50"
+  integrity sha512-H/+L+UkTV33uf49PH5pCAUBVPNj2nDBXTN+qS1dOwyyg24l3CcicicCA7ca+HMvJBZcFgl5r8e+RR6elsb4Lyw==
+  dependencies:
+    "@types/node" "*"
+
+"@types/semver@^7.5.6":
+  version "7.5.8"
+  resolved "https://registry.yarnpkg.com/@types/semver/-/semver-7.5.8.tgz#8268a8c57a3e4abd25c165ecd36237db7948a55e"
+  integrity sha512-I8EUhyrgfLrcTkzV3TSsGyl1tSuPrEDzr0yd5m90UgNxQkyDXULk3b6MlQqTCpZpNtWe1K0hzclnZkTcLBe2UQ==
+
+"@types/tmp@^0.2.6":
+  version "0.2.6"
+  resolved "https://registry.yarnpkg.com/@types/tmp/-/tmp-0.2.6.tgz#d785ee90c52d7cc020e249c948c36f7b32d1e217"
+  integrity sha512-chhaNf2oKHlRkDGt+tiKE2Z5aJ6qalm7Z9rlLdBwmOiAAf09YQvvoLXjWK4HWPF1xU/fqvMgfNfpVoBscA/tKA==
+
+abbrev@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/abbrev/-/abbrev-2.0.0.tgz#cf59829b8b4f03f89dda2771cb7f3653828c89bf"
+  integrity sha512-6/mh1E2u2YgEsCHdY0Yx5oW+61gZU+1vXaoiHHrpKeuRNNgFvS+/jrwHiQhB5apAf5oB7UB7E19ol2R2LKH8hQ==
+
+agent-base@^7.0.2, agent-base@^7.1.0:
+  version "7.1.0"
+  resolved "https://registry.yarnpkg.com/agent-base/-/agent-base-7.1.0.tgz#536802b76bc0b34aa50195eb2442276d613e3434"
+  integrity sha512-o/zjMZRhJxny7OyEF+Op8X+efiELC7k7yOjMzgfzVqOzXqkBkWI79YoTdOtsuWd5BWhAGAuOY/Xa6xpiaWXiNg==
+  dependencies:
+    debug "^4.3.4"
+
+aggregate-error@^3.0.0:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/aggregate-error/-/aggregate-error-3.1.0.tgz#92670ff50f5359bdb7a3e0d40d0ec30c5737687a"
+  integrity sha512-4I7Td01quW/RpocfNayFdFVk1qSuoh0E7JrbRJ16nH01HhKFQ88INq9Sd+nd72zqRySlr9BmDA8xlEJ6vJMrYA==
+  dependencies:
+    clean-stack "^2.0.0"
+    indent-string "^4.0.0"
+
+ansi-regex@^5.0.1:
+  version "5.0.1"
+  resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-5.0.1.tgz#082cb2c89c9fe8659a311a53bd6a4dc5301db304"
+  integrity sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==
+
+ansi-regex@^6.0.1:
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/ansi-regex/-/ansi-regex-6.0.1.tgz#3183e38fae9a65d7cb5e53945cd5897d0260a06a"
+  integrity sha512-n5M855fKb2SsfMIiFFoVrABHJC8QtHwVx+mHWP3QcEqBHYienj5dHSgjbxtC0WEZXYt4wcD6zrQElDPhFuZgfA==
+
+ansi-styles@^4.0.0:
+  version "4.3.0"
+  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-4.3.0.tgz#edd803628ae71c04c85ae7a0906edad34b648937"
+  integrity sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==
+  dependencies:
+    color-convert "^2.0.1"
+
+ansi-styles@^6.1.0:
+  version "6.2.1"
+  resolved "https://registry.yarnpkg.com/ansi-styles/-/ansi-styles-6.2.1.tgz#0e62320cf99c21afff3b3012192546aacbfb05c5"
+  integrity sha512-bN798gFfQX+viw3R7yrGWRqnrN2oRkEkUjjl4JNn4E8GxxbjtG3FbrEIIY3l8/hrwUwIeCZvi4QuOTP4MErVug==
+
+"aproba@^1.0.3 || ^2.0.0":
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/aproba/-/aproba-2.0.0.tgz#52520b8ae5b569215b354efc0caa3fe1e45a8adc"
+  integrity sha512-lYe4Gx7QT+MKGbDsA+Z+he/Wtef0BiwDOlK/XkBrdfsh9J/jPPXbX0tE9x9cl27Tmu5gg3QUbUrQYa/y+KOHPQ==
+
+are-we-there-yet@^4.0.0:
+  version "4.0.2"
+  resolved "https://registry.yarnpkg.com/are-we-there-yet/-/are-we-there-yet-4.0.2.tgz#aed25dd0eae514660d49ac2b2366b175c614785a"
+  integrity sha512-ncSWAawFhKMJDTdoAeOV+jyW1VCMj5QIAwULIBV0SSR7B/RLPPEQiknKcg/RIIZlUQrxELpsxMiTUoAQ4sIUyg==
+
+argparse@^1.0.7:
+  version "1.0.10"
+  resolved "https://registry.yarnpkg.com/argparse/-/argparse-1.0.10.tgz#bcd6791ea5ae09725e17e5ad988134cd40b3d911"
+  integrity sha512-o5Roy6tNG4SL/FOkCAN6RzjiakZS25RLYFrcMttJqbdd8BWrnA+fGz57iN5Pb06pvBGvl5gQ0B48dJlslXvoTg==
+  dependencies:
+    sprintf-js "~1.0.2"
+
+arrify@^1.0.0:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/arrify/-/arrify-1.0.1.tgz#898508da2226f380df904728456849c1501a4b0d"
+  integrity sha512-3CYzex9M9FGQjCGMGyi6/31c8GJbgb0qGyrx5HWxPd0aCwh4cB2YjMb2Xf9UuoogrMrlO9cTqnB5rI5GHZTcUA==
+
+available-typed-arrays@^1.0.7:
+  version "1.0.7"
+  resolved "https://registry.yarnpkg.com/available-typed-arrays/-/available-typed-arrays-1.0.7.tgz#a5cc375d6a03c2efc87a553f3e0b1522def14846"
+  integrity sha512-wvUjBtSGN7+7SjNpq/9M2Tg350UZD3q62IFZLbRAR1bSMlCo1ZaeW+BJ+D090e4hIIZLBcTDWe4Mh4jvUDajzQ==
+  dependencies:
+    possible-typed-array-names "^1.0.0"
+
+aws-sdk@^2.1450.0:
+  version "2.1582.0"
+  resolved "https://registry.yarnpkg.com/aws-sdk/-/aws-sdk-2.1582.0.tgz#194576a4137b63bc8225264794bfb4bc3c5e3450"
+  integrity sha512-oPSk5e1D0oegX3lC+pUIKmI5e4depLi0+9fU7C73KDq65nq8NmdehpbytPHGPqfftqfa3FTEXb2yUPA4A9wM9w==
+  dependencies:
+    buffer "4.9.2"
+    events "1.1.1"
+    ieee754 "1.1.13"
+    jmespath "0.16.0"
+    querystring "0.2.0"
+    sax "1.2.1"
+    url "0.10.3"
+    util "^0.12.4"
+    uuid "8.0.0"
+    xml2js "0.6.2"
+
+balanced-match@^1.0.0:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/balanced-match/-/balanced-match-1.0.2.tgz#e83e3a7e3f300b34cb9d87f615fa0cbf357690ee"
+  integrity sha512-3oSeUO0TMV67hN1AmbXsK4yaqU7tjiHlbxRDZOpH0KW9+CeX4bRAaX0Anxt0tx2MrpRpWwQaPwIlISEJhYU5Pw==
+
+base64-js@^1.0.2:
+  version "1.5.1"
+  resolved "https://registry.yarnpkg.com/base64-js/-/base64-js-1.5.1.tgz#1b1b440160a5bf7ad40b650f095963481903930a"
+  integrity sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==
+
+bin-links@^4.0.1:
+  version "4.0.3"
+  resolved "https://registry.yarnpkg.com/bin-links/-/bin-links-4.0.3.tgz#9e4a3c5900830aee3d7f52178b65e01dcdde64a5"
+  integrity sha512-obsRaULtJurnfox/MDwgq6Yo9kzbv1CPTk/1/s7Z/61Lezc8IKkFCOXNeVLXz0456WRzBQmSsDWlai2tIhBsfA==
+  dependencies:
+    cmd-shim "^6.0.0"
+    npm-normalize-package-bin "^3.0.0"
+    read-cmd-shim "^4.0.0"
+    write-file-atomic "^5.0.0"
+
+bowser@^2.11.0:
+  version "2.11.0"
+  resolved "https://registry.yarnpkg.com/bowser/-/bowser-2.11.0.tgz#5ca3c35757a7aa5771500c70a73a9f91ef420a8f"
+  integrity sha512-AlcaJBi/pqqJBIQ8U9Mcpc9i8Aqxn88Skv5d+xBX006BY5u8N3mGLHa5Lgppa7L/HfwgwLgZ6NYs+Ag6uUmJRA==
+
+brace-expansion@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/brace-expansion/-/brace-expansion-2.0.1.tgz#1edc459e0f0c548486ecf9fc99f2221364b9a0ae"
+  integrity sha512-XnAIvQ8eM+kC6aULx6wuQiwVsnzsi9d3WxzV3FpWTGA19F621kwdbsAcFKXgKUHZWsy+mY6iL1sHTxWEFCytDA==
+  dependencies:
+    balanced-match "^1.0.0"
+
+buffer-from@^1.0.0, buffer-from@^1.1.0:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/buffer-from/-/buffer-from-1.1.2.tgz#2b146a6fd72e80b4f55d255f35ed59a3a9a41bd5"
+  integrity sha512-E+XQCRwSbaaiChtv6k6Dwgc+bx+Bs6vuKJHHl5kox/BaKbhiXzqQOwK4cO22yElGp2OCmjwVhT3HmxgyPGnJfQ==
+
+buffer@4.9.2:
+  version "4.9.2"
+  resolved "https://registry.yarnpkg.com/buffer/-/buffer-4.9.2.tgz#230ead344002988644841ab0244af8c44bbe3ef8"
+  integrity sha512-xq+q3SRMOxGivLhBNaUdC64hDTQwejJ+H0T/NB1XMtTVEwNTrfFF3gAxiyW0Bu/xWEGhjVKgUcMhCrUy2+uCWg==
+  dependencies:
+    base64-js "^1.0.2"
+    ieee754 "^1.1.4"
+    isarray "^1.0.0"
+
+builtin-modules@3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/builtin-modules/-/builtin-modules-3.0.0.tgz#1e587d44b006620d90286cc7a9238bbc6129cab1"
+  integrity sha512-hMIeU4K2ilbXV6Uv93ZZ0Avg/M91RaKXucQ+4me2Do1txxBDyDZWCBa5bJSLqoNTRpXTLwEzIk1KmloenDDjhg==
+
+builtins@^5.0.0:
+  version "5.0.1"
+  resolved "https://registry.yarnpkg.com/builtins/-/builtins-5.0.1.tgz#87f6db9ab0458be728564fa81d876d8d74552fa9"
+  integrity sha512-qwVpFEHNfhYJIzNRBvd2C1kyo6jz3ZSMPyyuR47OPdiKWlbYnZNyDWuyR175qDnAJLiCo5fBBqPb3RiXgWlkOQ==
+  dependencies:
+    semver "^7.0.0"
+
+cacache@^18.0.0:
+  version "18.0.2"
+  resolved "https://registry.yarnpkg.com/cacache/-/cacache-18.0.2.tgz#fd527ea0f03a603be5c0da5805635f8eef00c60c"
+  integrity sha512-r3NU8h/P+4lVUHfeRw1dtgQYar3DZMm4/cm2bZgOvrFC/su7budSOeqh52VJIC4U4iG1WWwV6vRW0znqBvxNuw==
+  dependencies:
+    "@npmcli/fs" "^3.1.0"
+    fs-minipass "^3.0.0"
+    glob "^10.2.2"
+    lru-cache "^10.0.1"
+    minipass "^7.0.3"
+    minipass-collect "^2.0.1"
+    minipass-flush "^1.0.5"
+    minipass-pipeline "^1.2.4"
+    p-map "^4.0.0"
+    ssri "^10.0.0"
+    tar "^6.1.11"
+    unique-filename "^3.0.0"
+
+cacheable-lookup@^5.0.3:
+  version "5.0.4"
+  resolved "https://registry.yarnpkg.com/cacheable-lookup/-/cacheable-lookup-5.0.4.tgz#5a6b865b2c44357be3d5ebc2a467b032719a7005"
+  integrity sha512-2/kNscPhpcxrOigMZzbiWF7dz8ilhb/nIHU3EyZiXWXpeq/au8qJ8VhdftMkty3n7Gj6HIGalQG8oiBNB3AJgA==
+
+cacheable-request@^7.0.2:
+  version "7.0.4"
+  resolved "https://registry.yarnpkg.com/cacheable-request/-/cacheable-request-7.0.4.tgz#7a33ebf08613178b403635be7b899d3e69bbe817"
+  integrity sha512-v+p6ongsrp0yTGbJXjgxPow2+DL93DASP4kXCDKb8/bwRtt9OEF3whggkkDkGNzgcWy2XaF4a8nZglC7uElscg==
+  dependencies:
+    clone-response "^1.0.2"
+    get-stream "^5.1.0"
+    http-cache-semantics "^4.0.0"
+    keyv "^4.0.0"
+    lowercase-keys "^2.0.0"
+    normalize-url "^6.0.1"
+    responselike "^2.0.0"
+
+call-bind@^1.0.2, call-bind@^1.0.7:
+  version "1.0.7"
+  resolved "https://registry.yarnpkg.com/call-bind/-/call-bind-1.0.7.tgz#06016599c40c56498c18769d2730be242b6fa3b9"
+  integrity sha512-GHTSNSYICQ7scH7sZ+M2rFopRoLh8t2bLSW6BbgrtLsahOIB5iyAVJf9GjWK3cYTDaMj4XdBpM1cA6pIS0Kv2w==
+  dependencies:
+    es-define-property "^1.0.0"
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
+    get-intrinsic "^1.2.4"
+    set-function-length "^1.2.1"
+
+chownr@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/chownr/-/chownr-2.0.0.tgz#15bfbe53d2eab4cf70f18a8cd68ebe5b3cb1dece"
+  integrity sha512-bIomtDF5KGpdogkLd9VspvFzk9KfpyyGlS8YFVZl7TGPBHL5snIOnxeshwVgPteQ9b4Eydl+pVbIyE1DcvCWgQ==
+
+clean-stack@^2.0.0:
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/clean-stack/-/clean-stack-2.2.0.tgz#ee8472dbb129e727b31e8a10a427dee9dfe4008b"
+  integrity sha512-4diC9HaTE+KRAMWhDhrGOECgWZxoevMc5TlkObMqNSsVU62PYzXZ/SMTjzyGAFF1YusgxGcSWTEXBhp0CPwQ1A==
+
+cliui@^8.0.1:
+  version "8.0.1"
+  resolved "https://registry.yarnpkg.com/cliui/-/cliui-8.0.1.tgz#0c04b075db02cbfe60dc8e6cf2f5486b1a3608aa"
+  integrity sha512-BSeNnyus75C4//NQ9gQt1/csTXyo/8Sb+afLAkzAptFuMsod9HFokGNudZpi/oQV73hnVK+sR+5PVRMd+Dr7YQ==
+  dependencies:
+    string-width "^4.2.0"
+    strip-ansi "^6.0.1"
+    wrap-ansi "^7.0.0"
+
+clone-response@^1.0.2:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/clone-response/-/clone-response-1.0.3.tgz#af2032aa47816399cf5f0a1d0db902f517abb8c3"
+  integrity sha512-ROoL94jJH2dUVML2Y/5PEDNaSHgeOdSDicUyS7izcF63G6sTc/FTjLub4b8Il9S8S0beOfYt0TaA5qvFK+w0wA==
+  dependencies:
+    mimic-response "^1.0.0"
+
+cmd-shim@^6.0.0:
+  version "6.0.2"
+  resolved "https://registry.yarnpkg.com/cmd-shim/-/cmd-shim-6.0.2.tgz#435fd9e5c95340e61715e19f90209ed6fcd9e0a4"
+  integrity sha512-+FFYbB0YLaAkhkcrjkyNLYDiOsFSfRjwjY19LXk/psmMx1z00xlCv7hhQoTGXXIKi+YXHL/iiFo8NqMVQX9nOw==
+
+color-convert@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/color-convert/-/color-convert-2.0.1.tgz#72d3a68d598c9bdb3af2ad1e84f21d896abd4de3"
+  integrity sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==
+  dependencies:
+    color-name "~1.1.4"
+
+color-name@~1.1.4:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
+  integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
+
+color-support@^1.1.3:
+  version "1.1.3"
+  resolved "https://registry.yarnpkg.com/color-support/-/color-support-1.1.3.tgz#93834379a1cc9a0c61f82f52f0d04322251bd5a2"
+  integrity sha512-qiBjkpbMLO/HL68y+lh4q0/O1MZFj2RX6X/KmMa3+gJD3z+WwI1ZzDHysvqHGS3mP6mznPckpXmw1nI9cJjyRg==
+
+common-ancestor-path@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/common-ancestor-path/-/common-ancestor-path-1.0.1.tgz#4f7d2d1394d91b7abdf51871c62f71eadb0182a7"
+  integrity sha512-L3sHRo1pXXEqX8VU28kfgUY+YGsk09hPqZiZmLacNib6XNTCM8ubYeT7ryXQw8asB1sKgcU5lkB7ONug08aB8w==
+
+console-control-strings@^1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/console-control-strings/-/console-control-strings-1.1.0.tgz#3d7cf4464db6446ea644bf4b39507f9851008e8e"
+  integrity sha512-ty/fTekppD2fIwRvnZAVdeOiGd1c7YXEixbgJTNzqcxJWKQnjJ/V1bNEEE6hygpM3WjwHFUVK6HTjWSzV4a8sQ==
+
+cross-spawn@^7.0.0, cross-spawn@^7.0.3:
+  version "7.0.3"
+  resolved "https://registry.yarnpkg.com/cross-spawn/-/cross-spawn-7.0.3.tgz#f73a85b9d5d41d045551c177e2882d4ac85728a6"
+  integrity sha512-iRDPJKUPVEND7dHPO8rkbOnPpyDygcDFtWjpeWNCgy8WP2rXcxXL8TskReQl6OrB2G7+UJrags1q15Fudc7G6w==
+  dependencies:
+    path-key "^3.1.0"
+    shebang-command "^2.0.0"
+    which "^2.0.1"
+
+cssesc@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/cssesc/-/cssesc-3.0.0.tgz#37741919903b868565e1c09ea747445cd18983ee"
+  integrity sha512-/Tb/JcjK111nNScGob5MNtsntNM1aCNUDipB/TkwZFhyDrrE47SOx/18wF2bbjgc3ZzCSKW1T5nt5EbFoAz/Vg==
+
+debug@4, debug@^4.1.1, debug@^4.3.4:
+  version "4.3.4"
+  resolved "https://registry.yarnpkg.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
+  integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==
+  dependencies:
+    ms "2.1.2"
+
+decompress-response@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/decompress-response/-/decompress-response-6.0.0.tgz#ca387612ddb7e104bd16d85aab00d5ecf09c66fc"
+  integrity sha512-aW35yZM6Bb/4oJlZncMH2LCoZtJXTRxES17vE3hoRiowU2kWHaJKFkSBDnDR+cm9J+9QhXmREyIfv0pji9ejCQ==
+  dependencies:
+    mimic-response "^3.1.0"
+
+defer-to-connect@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/defer-to-connect/-/defer-to-connect-2.0.1.tgz#8016bdb4143e4632b77a3449c6236277de520587"
+  integrity sha512-4tvttepXG1VaYGrRibk5EwJd1t4udunSOVMdLSAL6mId1ix438oPwPZMALY41FCijukO1L0twNcGsdzS7dHgDg==
+
+define-data-property@^1.1.4:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/define-data-property/-/define-data-property-1.1.4.tgz#894dc141bb7d3060ae4366f6a0107e68fbe48c5e"
+  integrity sha512-rBMvIzlpA8v6E+SJZoo++HAYqsLrkg7MSfIinMPFhmkorw7X+dOXVJQs+QT69zGkzMyfDnIMN2Wid1+NbL3T+A==
+  dependencies:
+    es-define-property "^1.0.0"
+    es-errors "^1.3.0"
+    gopd "^1.0.1"
+
+diff@^3.1.0:
+  version "3.5.0"
+  resolved "https://registry.yarnpkg.com/diff/-/diff-3.5.0.tgz#800c0dd1e0a8bfbc95835c202ad220fe317e5a12"
+  integrity sha512-A46qtFgd+g7pDZinpnwiRJtxbC1hpgf0uzP3iG89scHk0AUC7A1TGxf5OiiOUv/JMZR8GOt8hL900hV0bOy5xA==
+
+"docker-classic@npm:@pulumi/docker@3.6.1":
+  version "3.6.1"
+  resolved "https://registry.yarnpkg.com/@pulumi/docker/-/docker-3.6.1.tgz#a216c9bd2bbd35dd7091db2827a3a434adff021c"
+  integrity sha512-BZME50QkT556v+LvmTXPT8ssB2xxNkp9+msB5xYFEnUnWcdGAx5yUysQw70RJCb+U0GbkJSbxtlgMJgOQf/now==
+  dependencies:
+    "@pulumi/pulumi" "^3.0.0"
+    semver "^5.4.0"
+
+eastasianwidth@^0.2.0:
+  version "0.2.0"
+  resolved "https://registry.yarnpkg.com/eastasianwidth/-/eastasianwidth-0.2.0.tgz#696ce2ec0aa0e6ea93a397ffcf24aa7840c827cb"
+  integrity sha512-I88TYZWc9XiYHRQ4/3c5rjjfgkjhLyW2luGIheGERbNQ6OY7yTybanSpDXZa8y7VUP9YmDcYa+eyq4ca7iLqWA==
+
+emoji-regex@^8.0.0:
+  version "8.0.0"
+  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-8.0.0.tgz#e818fd69ce5ccfcb404594f842963bf53164cc37"
+  integrity sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==
+
+emoji-regex@^9.2.2:
+  version "9.2.2"
+  resolved "https://registry.yarnpkg.com/emoji-regex/-/emoji-regex-9.2.2.tgz#840c8803b0d8047f4ff0cf963176b32d4ef3ed72"
+  integrity sha512-L18DaJsXSUk2+42pv8mLs5jJT2hqFkFE4j21wOmgbUqsZ2hL72NsUU785g9RXgo3s0ZNgVl42TiHp3ZtOv/Vyg==
+
+encoding@^0.1.13:
+  version "0.1.13"
+  resolved "https://registry.yarnpkg.com/encoding/-/encoding-0.1.13.tgz#56574afdd791f54a8e9b2785c0582a2d26210fa9"
+  integrity sha512-ETBauow1T35Y/WZMkio9jiM0Z5xjHHmJ4XmjZOq1l/dXz3lr2sRn87nJy20RupqSh1F2m3HHPSp8ShIPQJrJ3A==
+  dependencies:
+    iconv-lite "^0.6.2"
+
+end-of-stream@^1.1.0:
+  version "1.4.4"
+  resolved "https://registry.yarnpkg.com/end-of-stream/-/end-of-stream-1.4.4.tgz#5ae64a5f45057baf3626ec14da0ca5e4b2431eb0"
+  integrity sha512-+uw1inIHVPQoaVuHzRyXd21icM+cnt4CzD5rW+NC1wjOUSTOs+Te7FOv7AhN7vS9x/oIyhLP5PR1H+phQAHu5Q==
+  dependencies:
+    once "^1.4.0"
+
+env-paths@^2.2.0:
+  version "2.2.1"
+  resolved "https://registry.yarnpkg.com/env-paths/-/env-paths-2.2.1.tgz#420399d416ce1fbe9bc0a07c62fa68d67fd0f8f2"
+  integrity sha512-+h1lkLKhZMTYjog1VEpJNG7NZJWcuc2DDk/qsqSTRRCOXiLjeQ1d1/udrUGhqMxUgAlwKNZ0cf2uqan5GLuS2A==
+
+err-code@^2.0.2:
+  version "2.0.3"
+  resolved "https://registry.yarnpkg.com/err-code/-/err-code-2.0.3.tgz#23c2f3b756ffdfc608d30e27c9a941024807e7f9"
+  integrity sha512-2bmlRpNKBxT/CRmPOlyISQpNj+qSeYvcym/uT0Jx2bMOlKLtSy1ZmLuVxSEKKyor/N5yhvp/ZiG1oE3DEYMSFA==
+
+es-define-property@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/es-define-property/-/es-define-property-1.0.0.tgz#c7faefbdff8b2696cf5f46921edfb77cc4ba3845"
+  integrity sha512-jxayLKShrEqqzJ0eumQbVhTYQM27CfT1T35+gCgDFoL82JLsXqTJ76zv6A0YLOgEnLUMvLzsDsGIrl8NFpT2gQ==
+  dependencies:
+    get-intrinsic "^1.2.4"
+
+es-errors@^1.3.0:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/es-errors/-/es-errors-1.3.0.tgz#05f75a25dab98e4fb1dcd5e1472c0546d5057c8f"
+  integrity sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==
+
+escalade@^3.1.1:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.2.tgz#54076e9ab29ea5bf3d8f1ed62acffbb88272df27"
+  integrity sha512-ErCHMCae19vR8vQGe50xIsVomy19rg6gFu3+r3jkEO46suLMWBksvVyoGgQV+jOfl84ZSOSlmv6Gxa89PmTGmA==
+
+esprima@^4.0.0:
+  version "4.0.1"
+  resolved "https://registry.yarnpkg.com/esprima/-/esprima-4.0.1.tgz#13b04cdb3e6c5d19df91ab6987a8695619b0aa71"
+  integrity sha512-eGuFFw7Upda+g4p+QHvnW0RyTX/SVeJBDM/gCtMARO0cLuT2HcEKnTPvhjV6aGeqrCB/sbNop0Kszm0jsaWU4A==
+
+events@1.1.1:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/events/-/events-1.1.1.tgz#9ebdb7635ad099c70dcc4c2a1f5004288e8bd924"
+  integrity sha512-kEcvvCBByWXGnZy6JUlgAp2gBIUjfCAV6P6TgT1/aaQKcmuAEC4OZTV1I4EWQLz2gxZw76atuVyvHhTxvi0Flw==
+
+execa@^5.1.0:
+  version "5.1.1"
+  resolved "https://registry.yarnpkg.com/execa/-/execa-5.1.1.tgz#f80ad9cbf4298f7bd1d4c9555c21e93741c411dd"
+  integrity sha512-8uSpZZocAZRBAPIEINJj3Lo9HyGitllczc27Eh5YYojjMFMn8yHMDMaUHE2Jqfq05D/wucwI4JGURyXt1vchyg==
+  dependencies:
+    cross-spawn "^7.0.3"
+    get-stream "^6.0.0"
+    human-signals "^2.1.0"
+    is-stream "^2.0.0"
+    merge-stream "^2.0.0"
+    npm-run-path "^4.0.1"
+    onetime "^5.1.2"
+    signal-exit "^3.0.3"
+    strip-final-newline "^2.0.0"
+
+exponential-backoff@^3.1.1:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/exponential-backoff/-/exponential-backoff-3.1.1.tgz#64ac7526fe341ab18a39016cd22c787d01e00bf6"
+  integrity sha512-dX7e/LHVJ6W3DE1MHWi9S1EYzDESENfLrYohG2G++ovZrYOkm4Knwa0mc1cn84xJOR4KEU0WSchhLbd0UklbHw==
+
+fast-xml-parser@4.2.5:
+  version "4.2.5"
+  resolved "https://registry.yarnpkg.com/fast-xml-parser/-/fast-xml-parser-4.2.5.tgz#a6747a09296a6cb34f2ae634019bf1738f3b421f"
+  integrity sha512-B9/wizE4WngqQftFPmdaMYlXoJlJOYxGQOanC77fq9k8+Z0v5dDSVh+3glErdIROP//s/jgb7ZuxKfB8nVyo0g==
+  dependencies:
+    strnum "^1.0.5"
+
+fdir@^6.1.1:
+  version "6.1.1"
+  resolved "https://registry.yarnpkg.com/fdir/-/fdir-6.1.1.tgz#316b58145a05223b75c8b371e80bb3bad8f1441e"
+  integrity sha512-QfKBVg453Dyn3mr0Q0O+Tkr1r79lOTAKSi9f/Ot4+qVEwxWhav2Z+SudrG9vQjM2aYRMQQZ2/Q1zdA8ACM1pDg==
+
+find-up@^6.3.0:
+  version "6.3.0"
+  resolved "https://registry.yarnpkg.com/find-up/-/find-up-6.3.0.tgz#2abab3d3280b2dc7ac10199ef324c4e002c8c790"
+  integrity sha512-v2ZsoEuVHYy8ZIlYqwPe/39Cy+cFDzp4dXPaxNvkEuouymu+2Jbz0PxpKarJHYJTmv2HWT3O382qY8l4jMWthw==
+  dependencies:
+    locate-path "^7.1.0"
+    path-exists "^5.0.0"
+
+for-each@^0.3.3:
+  version "0.3.3"
+  resolved "https://registry.yarnpkg.com/for-each/-/for-each-0.3.3.tgz#69b447e88a0a5d32c3e7084f3f1710034b21376e"
+  integrity sha512-jqYfLp7mo9vIyQf8ykW2v7A+2N4QjeCeI5+Dz9XraiO1ign81wjiH7Fb9vSOWvQfNtmSa4H2RoQTrrXivdUZmw==
+  dependencies:
+    is-callable "^1.1.3"
+
+foreground-child@^3.1.0:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/foreground-child/-/foreground-child-3.1.1.tgz#1d173e776d75d2772fed08efe4a0de1ea1b12d0d"
+  integrity sha512-TMKDUnIte6bfb5nWv7V/caI169OHgvwjb7V4WkeUvbQQdjr5rWKqHFiKWb/fcOwB+CzBT+qbWjvj+DVwRskpIg==
+  dependencies:
+    cross-spawn "^7.0.0"
+    signal-exit "^4.0.1"
+
+fs-minipass@^2.0.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-2.1.0.tgz#7f5036fdbf12c63c169190cbe4199c852271f9fb"
+  integrity sha512-V/JgOLFCS+R6Vcq0slCuaeWEdNC3ouDlJMNIsacH2VtALiu9mV4LPrHc5cDl8k5aw6J8jwgWWpiTo5RYhmIzvg==
+  dependencies:
+    minipass "^3.0.0"
+
+fs-minipass@^3.0.0:
+  version "3.0.3"
+  resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-3.0.3.tgz#79a85981c4dc120065e96f62086bf6f9dc26cc54"
+  integrity sha512-XUBA9XClHbnJWSfBzjkm6RvPsyg3sryZt06BEQoXcF7EK/xpGaQYJgQKDJSUH5SGZ76Y7pFx1QBnXz09rU5Fbw==
+  dependencies:
+    minipass "^7.0.3"
+
+function-bind@^1.1.2:
+  version "1.1.2"
+  resolved "https://registry.yarnpkg.com/function-bind/-/function-bind-1.1.2.tgz#2c02d864d97f3ea6c8830c464cbd11ab6eab7a1c"
+  integrity sha512-7XHNxH7qX9xG5mIwxkhumTox/MIRNcOgDrxWsMt2pAr23WHp6MrRlN7FBSFpCpr+oVO0F744iUgR82nJMfG2SA==
+
+gauge@^5.0.0:
+  version "5.0.1"
+  resolved "https://registry.yarnpkg.com/gauge/-/gauge-5.0.1.tgz#1efc801b8ff076b86ef3e9a7a280a975df572112"
+  integrity sha512-CmykPMJGuNan/3S4kZOpvvPYSNqSHANiWnh9XcMU2pSjtBfF0XzZ2p1bFAxTbnFxyBuPxQYHhzwaoOmUdqzvxQ==
+  dependencies:
+    aproba "^1.0.3 || ^2.0.0"
+    color-support "^1.1.3"
+    console-control-strings "^1.1.0"
+    has-unicode "^2.0.1"
+    signal-exit "^4.0.1"
+    string-width "^4.2.3"
+    strip-ansi "^6.0.1"
+    wide-align "^1.1.5"
+
+get-caller-file@^2.0.5:
+  version "2.0.5"
+  resolved "https://registry.yarnpkg.com/get-caller-file/-/get-caller-file-2.0.5.tgz#4f94412a82db32f36e3b0b9741f8a97feb031f7e"
+  integrity sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==
+
+get-intrinsic@^1.1.3, get-intrinsic@^1.2.4:
+  version "1.2.4"
+  resolved "https://registry.yarnpkg.com/get-intrinsic/-/get-intrinsic-1.2.4.tgz#e385f5a4b5227d449c3eabbad05494ef0abbeadd"
+  integrity sha512-5uYhsJH8VJBTv7oslg4BznJYhDoRI6waYCxMmCdnTrcCrHA/fCFKoTFz2JKKE0HdDFUF7/oQuhzumXJK7paBRQ==
+  dependencies:
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
+    has-proto "^1.0.1"
+    has-symbols "^1.0.3"
+    hasown "^2.0.0"
+
+get-stream@^5.1.0:
+  version "5.2.0"
+  resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-5.2.0.tgz#4966a1795ee5ace65e706c4b7beb71257d6e22d3"
+  integrity sha512-nBF+F1rAZVCu/p7rjzgA+Yb4lfYXrpl7a6VmJrU8wF9I1CKvP/QwPNZHnOlwbTkY6dvtFIzFMSyQXbLoTQPRpA==
+  dependencies:
+    pump "^3.0.0"
+
+get-stream@^6.0.0:
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/get-stream/-/get-stream-6.0.1.tgz#a262d8eef67aced57c2852ad6167526a43cbf7b7"
+  integrity sha512-ts6Wi+2j3jQjqi70w5AlN8DFnkSwC+MqmxEzdEALB2qXZYV3X/b1CTfgPLGJNMeAWxdPfU8FO1ms3NUfaHCPYg==
+
+glob@^10.2.2, glob@^10.3.10:
+  version "10.3.10"
+  resolved "https://registry.yarnpkg.com/glob/-/glob-10.3.10.tgz#0351ebb809fd187fe421ab96af83d3a70715df4b"
+  integrity sha512-fa46+tv1Ak0UPK1TOy/pZrIybNNt4HCv7SDzwyfiOZkvZLEbjsZkJBPtDHVshZjbecAoAGSC20MjLDG/qr679g==
+  dependencies:
+    foreground-child "^3.1.0"
+    jackspeak "^2.3.5"
+    minimatch "^9.0.1"
+    minipass "^5.0.0 || ^6.0.2 || ^7.0.0"
+    path-scurry "^1.10.1"
+
+google-protobuf@^3.5.0:
+  version "3.21.2"
+  resolved "https://registry.yarnpkg.com/google-protobuf/-/google-protobuf-3.21.2.tgz#4580a2bea8bbb291ee579d1fefb14d6fa3070ea4"
+  integrity sha512-3MSOYFO5U9mPGikIYCzK0SaThypfGgS6bHqrUGXG3DPHCrb+txNqeEcns1W0lkGfk0rCyNXm7xB9rMxnCiZOoA==
+
+gopd@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/gopd/-/gopd-1.0.1.tgz#29ff76de69dac7489b7c0918a5788e56477c332c"
+  integrity sha512-d65bNlIadxvpb/A2abVdlqKqV563juRnZ1Wtk6s1sIR8uNsXR70xqIzVqxVf1eTqDunwT2MkczEeaezCKTZhwA==
+  dependencies:
+    get-intrinsic "^1.1.3"
+
+got@^11.8.6:
+  version "11.8.6"
+  resolved "https://registry.yarnpkg.com/got/-/got-11.8.6.tgz#276e827ead8772eddbcfc97170590b841823233a"
+  integrity sha512-6tfZ91bOr7bOXnK7PRDCGBLa1H4U080YHNaAQ2KsMGlLEzRbk44nsZF2E1IeRc3vtJHPVbKCYgdFbaGO2ljd8g==
+  dependencies:
+    "@sindresorhus/is" "^4.0.0"
+    "@szmarczak/http-timer" "^4.0.5"
+    "@types/cacheable-request" "^6.0.1"
+    "@types/responselike" "^1.0.0"
+    cacheable-lookup "^5.0.3"
+    cacheable-request "^7.0.2"
+    decompress-response "^6.0.0"
+    http2-wrapper "^1.0.0-beta.5.2"
+    lowercase-keys "^2.0.0"
+    p-cancelable "^2.0.0"
+    responselike "^2.0.0"
+
+graceful-fs@^4.2.6:
+  version "4.2.11"
+  resolved "https://registry.yarnpkg.com/graceful-fs/-/graceful-fs-4.2.11.tgz#4183e4e8bf08bb6e05bbb2f7d2e0c8f712ca40e3"
+  integrity sha512-RbJ5/jmFcNNCcDV5o9eTnBLJ/HszWV0P73bc+Ff4nS/rJj+YaS6IGyiOL0VoBYX+l1Wrl3k63h/KrH+nhJ0XvQ==
+
+has-property-descriptors@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz#963ed7d071dc7bf5f084c5bfbe0d1b6222586854"
+  integrity sha512-55JNKuIW+vq4Ke1BjOTjM2YctQIvCT7GFzHwmfZPGo5wnrgkid0YQtnAleFSqumZm4az3n2BS+erby5ipJdgrg==
+  dependencies:
+    es-define-property "^1.0.0"
+
+has-proto@^1.0.1:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/has-proto/-/has-proto-1.0.3.tgz#b31ddfe9b0e6e9914536a6ab286426d0214f77fd"
+  integrity sha512-SJ1amZAJUiZS+PhsVLf5tGydlaVB8EdFpaSO4gmiUKUOxk8qzn5AIy4ZeJUmh22znIdk/uMAUT2pl3FxzVUH+Q==
+
+has-symbols@^1.0.3:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.3.tgz#bb7b2c4349251dce87b125f7bdf874aa7c8b39f8"
+  integrity sha512-l3LCuF6MgDNwTDKkdYGEihYjt5pRPbEg46rtlmnSPlUbgmB8LOIrKJbYYFBSbnPaJexMKtiPO8hmeRjRz2Td+A==
+
+has-tostringtag@^1.0.0, has-tostringtag@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/has-tostringtag/-/has-tostringtag-1.0.2.tgz#2cdc42d40bef2e5b4eeab7c01a73c54ce7ab5abc"
+  integrity sha512-NqADB8VjPFLM2V0VvHUewwwsw0ZWBaIdgo+ieHtK3hasLz4qeCRjYcqfB6AQrBggRKppKF8L52/VqdVsO47Dlw==
+  dependencies:
+    has-symbols "^1.0.3"
+
+has-unicode@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/has-unicode/-/has-unicode-2.0.1.tgz#e0e6fe6a28cf51138855e086d1691e771de2a8b9"
+  integrity sha512-8Rf9Y83NBReMnx0gFzA8JImQACstCYWUplepDa9xprwwtmgEZUF0h/i5xSA625zB/I37EtrswSST6OXxwaaIJQ==
+
+hasown@^2.0.0:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/hasown/-/hasown-2.0.2.tgz#003eaf91be7adc372e84ec59dc37252cedb80003"
+  integrity sha512-0hJU9SCPvmMzIBdZFqNPXWa6dqh7WdH0cII9y+CyS8rG3nL48Bclra9HmKhVVUHyPWNH5Y7xDwAB7bfgSjkUMQ==
+  dependencies:
+    function-bind "^1.1.2"
+
+hosted-git-info@^7.0.0, hosted-git-info@^7.0.1:
+  version "7.0.1"
+  resolved "https://registry.yarnpkg.com/hosted-git-info/-/hosted-git-info-7.0.1.tgz#9985fcb2700467fecf7f33a4d4874e30680b5322"
+  integrity sha512-+K84LB1DYwMHoHSgaOY/Jfhw3ucPmSET5v98Ke/HdNSw4a0UktWzyW1mjhjpuxxTqOOsfWT/7iVshHmVZ4IpOA==
+  dependencies:
+    lru-cache "^10.0.1"
+
+http-cache-semantics@^4.0.0, http-cache-semantics@^4.1.1:
+  version "4.1.1"
+  resolved "https://registry.yarnpkg.com/http-cache-semantics/-/http-cache-semantics-4.1.1.tgz#abe02fcb2985460bf0323be664436ec3476a6d5a"
+  integrity sha512-er295DKPVsV82j5kw1Gjt+ADA/XYHsajl82cGNQG2eyoPkvgUhX+nDIyelzhIWbbsXP39EHcI6l5tYs2FYqYXQ==
+
+http-proxy-agent@^7.0.0:
+  version "7.0.2"
+  resolved "https://registry.yarnpkg.com/http-proxy-agent/-/http-proxy-agent-7.0.2.tgz#9a8b1f246866c028509486585f62b8f2c18c270e"
+  integrity sha512-T1gkAiYYDWYx3V5Bmyu7HcfcvL7mUrTWiM6yOfa3PIphViJ/gFPbvidQ+veqSOHci/PxBcDabeUNCzpOODJZig==
+  dependencies:
+    agent-base "^7.1.0"
+    debug "^4.3.4"
+
+http2-wrapper@^1.0.0-beta.5.2:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/http2-wrapper/-/http2-wrapper-1.0.3.tgz#b8f55e0c1f25d4ebd08b3b0c2c079f9590800b3d"
+  integrity sha512-V+23sDMr12Wnz7iTcDeJr3O6AIxlnvT/bmaAAAP/Xda35C90p9599p0F1eHR/N1KILWSoWVAiOMFjBBXaXSMxg==
+  dependencies:
+    quick-lru "^5.1.1"
+    resolve-alpn "^1.0.0"
+
+https-proxy-agent@^7.0.1:
+  version "7.0.4"
+  resolved "https://registry.yarnpkg.com/https-proxy-agent/-/https-proxy-agent-7.0.4.tgz#8e97b841a029ad8ddc8731f26595bad868cb4168"
+  integrity sha512-wlwpilI7YdjSkWaQ/7omYBMTliDcmCN8OLihO6I9B86g06lMyAoqgoDpV0XqoaPOKj+0DIdAvnsWfyAAhmimcg==
+  dependencies:
+    agent-base "^7.0.2"
+    debug "4"
+
+human-signals@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/human-signals/-/human-signals-2.1.0.tgz#dc91fcba42e4d06e4abaed33b3e7a3c02f514ea0"
+  integrity sha512-B4FFZ6q/T2jhhksgkbEW3HBvWIfDW85snkQgawt07S7J5QXTk6BkNV+0yAeZrM5QpMAdYlocGoljn0sJ/WQkFw==
+
+iconv-lite@^0.6.2:
+  version "0.6.3"
+  resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.6.3.tgz#a52f80bf38da1952eb5c681790719871a1a72501"
+  integrity sha512-4fCk79wshMdzMp2rH06qWrJE4iolqLhCUH+OiuIgU++RB0+94NlDL81atO7GX55uUKueo0txHNtvEyI6D7WdMw==
+  dependencies:
+    safer-buffer ">= 2.1.2 < 3.0.0"
+
+ieee754@1.1.13:
+  version "1.1.13"
+  resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.1.13.tgz#ec168558e95aa181fd87d37f55c32bbcb6708b84"
+  integrity sha512-4vf7I2LYV/HaWerSo3XmlMkp5eZ83i+/CDluXi/IGTs/O1sejBNhTtnxzmRZfvOUqj7lZjqHkeTvpgSFDlWZTg==
+
+ieee754@^1.1.4:
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/ieee754/-/ieee754-1.2.1.tgz#8eb7a10a63fff25d15a57b001586d177d1b0d352"
+  integrity sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==
+
+ignore-walk@^6.0.4:
+  version "6.0.4"
+  resolved "https://registry.yarnpkg.com/ignore-walk/-/ignore-walk-6.0.4.tgz#89950be94b4f522225eb63a13c56badb639190e9"
+  integrity sha512-t7sv42WkwFkyKbivUCglsQW5YWMskWtbEf4MNKX5u/CCWHKSPzN4FtBQGsQZgCLbxOzpVlcbWVK5KB3auIOjSw==
+  dependencies:
+    minimatch "^9.0.0"
+
+imurmurhash@^0.1.4:
+  version "0.1.4"
+  resolved "https://registry.yarnpkg.com/imurmurhash/-/imurmurhash-0.1.4.tgz#9218b9b2b928a238b13dc4fb6b6d576f231453ea"
+  integrity sha512-JmXMZ6wuvDmLiHEml9ykzqO6lwFbof0GG4IkcGaENdCRDDmMVnny7s5HsIgHCbaq0w2MyPhDqkhTUgS2LU2PHA==
+
+indent-string@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/indent-string/-/indent-string-4.0.0.tgz#624f8f4497d619b2d9768531d58f4122854d7251"
+  integrity sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==
+
+inherits@^2.0.3:
+  version "2.0.4"
+  resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.4.tgz#0fa2c64f932917c3433a0ded55363aae37416b7c"
+  integrity sha512-k/vGaX4/Yla3WzyMCvTQOXYeIHvqOKtnqBduzTHpzpQZzAskKMhZ2K+EnBiSM9zGSoIFeMpXKxa4dYeZIQqewQ==
+
+ini@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/ini/-/ini-2.0.0.tgz#e5fd556ecdd5726be978fa1001862eacb0a94bc5"
+  integrity sha512-7PnF4oN3CvZF23ADhA5wRaYEQpJ8qygSkbtTXWBeXWXmEVRXK+1ITciHWwHhsjv1TmW0MgacIv6hEi5pX5NQdA==
+
+ip-address@^9.0.5:
+  version "9.0.5"
+  resolved "https://registry.yarnpkg.com/ip-address/-/ip-address-9.0.5.tgz#117a960819b08780c3bd1f14ef3c1cc1d3f3ea5a"
+  integrity sha512-zHtQzGojZXTwZTHQqra+ETKd4Sn3vgi7uBmlPoXVWZqYvuKmtI0l/VZTjqGmJY9x88GGOaZ9+G9ES8hC4T4X8g==
+  dependencies:
+    jsbn "1.1.0"
+    sprintf-js "^1.1.3"
+
+is-arguments@^1.0.4:
+  version "1.1.1"
+  resolved "https://registry.yarnpkg.com/is-arguments/-/is-arguments-1.1.1.tgz#15b3f88fda01f2a97fec84ca761a560f123efa9b"
+  integrity sha512-8Q7EARjzEnKpt/PCD7e1cgUS0a6X8u5tdSiMqXhojOdoV9TsMsiO+9VLC5vAmO8N7/GmXn7yjR8qnA6bVAEzfA==
+  dependencies:
+    call-bind "^1.0.2"
+    has-tostringtag "^1.0.0"
+
+is-callable@^1.1.3:
+  version "1.2.7"
+  resolved "https://registry.yarnpkg.com/is-callable/-/is-callable-1.2.7.tgz#3bc2a85ea742d9e36205dcacdd72ca1fdc51b055"
+  integrity sha512-1BC0BVFhS/p0qtw6enp8e+8OD0UrK0oFLztSjNzhcKA3WDuJxxAPXzPuPtKkjEY9UUoEWlX/8fgKeu2S8i9JTA==
+
+is-core-module@^2.13.0, is-core-module@^2.8.1:
+  version "2.13.1"
+  resolved "https://registry.yarnpkg.com/is-core-module/-/is-core-module-2.13.1.tgz#ad0d7532c6fea9da1ebdc82742d74525c6273384"
+  integrity sha512-hHrIjvZsftOsvKSn2TRYl63zvxsgE0K+0mYMoH6gD4omR5IWB2KynivBQczo3+wF1cCkjzvptnI9Q0sPU66ilw==
+  dependencies:
+    hasown "^2.0.0"
+
+is-fullwidth-code-point@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/is-fullwidth-code-point/-/is-fullwidth-code-point-3.0.0.tgz#f116f8064fe90b3f7844a38997c0b75051269f1d"
+  integrity sha512-zymm5+u+sCsSWyD9qNaejV3DFvhCKclKdizYaJUuHA83RLjb7nSuGnddCHGv0hk+KY7BMAlsWeK4Ueg6EV6XQg==
+
+is-generator-function@^1.0.7:
+  version "1.0.10"
+  resolved "https://registry.yarnpkg.com/is-generator-function/-/is-generator-function-1.0.10.tgz#f1558baf1ac17e0deea7c0415c438351ff2b3c72"
+  integrity sha512-jsEjy9l3yiXEQ+PsXdmBwEPcOxaXWLspKdplFUVI9vq1iZgIekeC0L167qeu86czQaxed3q/Uzuw0swL0irL8A==
+  dependencies:
+    has-tostringtag "^1.0.0"
+
+is-lambda@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/is-lambda/-/is-lambda-1.0.1.tgz#3d9877899e6a53efc0160504cde15f82e6f061d5"
+  integrity sha512-z7CMFGNrENq5iFB9Bqo64Xk6Y9sg+epq1myIcdHaGnbMTYOxvzsEtdYqQUylB7LxfkvgrrjP32T6Ywciio9UIQ==
+
+is-stream@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/is-stream/-/is-stream-2.0.1.tgz#fac1e3d53b97ad5a9d0ae9cef2389f5810a5c077"
+  integrity sha512-hFoiJiTl63nn+kstHGBtewWSKnQLpyb155KHheA1l39uvtO9nWIop1p3udqPcUd/xbF1VLMO4n7OI6p7RbngDg==
+
+is-typed-array@^1.1.3:
+  version "1.1.13"
+  resolved "https://registry.yarnpkg.com/is-typed-array/-/is-typed-array-1.1.13.tgz#d6c5ca56df62334959322d7d7dd1cca50debe229"
+  integrity sha512-uZ25/bUAlUY5fR4OKT4rZQEBrzQWYV9ZJYGGsUmEJ6thodVJ1HX64ePQ6Z0qPWP+m+Uq6e9UugrE38jeYsDSMw==
+  dependencies:
+    which-typed-array "^1.1.14"
+
+isarray@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/isarray/-/isarray-1.0.0.tgz#bb935d48582cba168c06834957a54a3e07124f11"
+  integrity sha512-VLghIWNM6ELQzo7zwmcg0NmTVyWKYjvIeM83yjp0wRDTmUnrM678fQbcKBo6n2CJEF0szoG//ytg+TKla89ALQ==
+
+isexe@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/isexe/-/isexe-2.0.0.tgz#e8fbf374dc556ff8947a10dcb0572d633f2cfa10"
+  integrity sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw==
+
+isexe@^3.1.1:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/isexe/-/isexe-3.1.1.tgz#4a407e2bd78ddfb14bea0c27c6f7072dde775f0d"
+  integrity sha512-LpB/54B+/2J5hqQ7imZHfdU31OlgQqx7ZicVlkm9kzg9/w8GKLEcFfJl/t7DCEDueOyBAD6zCCwTO6Fzs0NoEQ==
+
+jackspeak@^2.3.5:
+  version "2.3.6"
+  resolved "https://registry.yarnpkg.com/jackspeak/-/jackspeak-2.3.6.tgz#647ecc472238aee4b06ac0e461acc21a8c505ca8"
+  integrity sha512-N3yCS/NegsOBokc8GAdM8UcmfsKiSS8cipheD/nivzr700H+nsMOxJjQnvwOcRYVuFkdH0wGUvW2WbXGmrZGbQ==
+  dependencies:
+    "@isaacs/cliui" "^8.0.2"
+  optionalDependencies:
+    "@pkgjs/parseargs" "^0.11.0"
+
+jmespath@0.16.0:
+  version "0.16.0"
+  resolved "https://registry.yarnpkg.com/jmespath/-/jmespath-0.16.0.tgz#b15b0a85dfd4d930d43e69ed605943c802785076"
+  integrity sha512-9FzQjJ7MATs1tSpnco1K6ayiYE3figslrXA72G2HQ/n76RzvYlofyi5QM+iX4YRs/pu3yzxlVQSST23+dMDknw==
+
+js-yaml@^3.14.0:
+  version "3.14.1"
+  resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-3.14.1.tgz#dae812fdb3825fa306609a8717383c50c36a0537"
+  integrity sha512-okMH7OXXJ7YrN9Ok3/SXrnu4iX9yOk+25nqX4imS2npuvTYDmo/QEZoqwZkYaIDk3jVvBOTOIEgEhaLOynBS9g==
+  dependencies:
+    argparse "^1.0.7"
+    esprima "^4.0.0"
+
+jsbn@1.1.0:
+  version "1.1.0"
+  resolved "https://registry.yarnpkg.com/jsbn/-/jsbn-1.1.0.tgz#b01307cb29b618a1ed26ec79e911f803c4da0040"
+  integrity sha512-4bYVV3aAMtDTTu4+xsDYa6sy9GyJ69/amsu9sYF2zqjiEoZA5xJi3BrfX3uY+/IekIu7MwdObdbDWpoZdBv3/A==
+
+json-buffer@3.0.1:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/json-buffer/-/json-buffer-3.0.1.tgz#9338802a30d3b6605fbe0613e094008ca8c05a13"
+  integrity sha512-4bV5BfR2mqfQTJm+V5tPPdf+ZpuhiIvTuAB5g8kcrXOZpTT/QwwVRWBywX1ozr6lEuPdbHxwaJlm9G6mI2sfSQ==
+
+json-parse-even-better-errors@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/json-parse-even-better-errors/-/json-parse-even-better-errors-3.0.1.tgz#02bb29fb5da90b5444581749c22cedd3597c6cb0"
+  integrity sha512-aatBvbL26wVUCLmbWdCpeu9iF5wOyWpagiKkInA+kfws3sWdBrTnsvN2CKcyCYyUrc7rebNBlK6+kteg7ksecg==
+
+json-stringify-nice@^1.1.4:
+  version "1.1.4"
+  resolved "https://registry.yarnpkg.com/json-stringify-nice/-/json-stringify-nice-1.1.4.tgz#2c937962b80181d3f317dd39aa323e14f5a60a67"
+  integrity sha512-5Z5RFW63yxReJ7vANgW6eZFGWaQvnPE3WNmZoOJrSkGju2etKA2L5rrOa1sm877TVTFt57A80BH1bArcmlLfPw==
+
+jsonparse@^1.3.1:
+  version "1.3.1"
+  resolved "https://registry.yarnpkg.com/jsonparse/-/jsonparse-1.3.1.tgz#3f4dae4a91fac315f71062f8521cc239f1366280"
+  integrity sha512-POQXvpdL69+CluYsillJ7SUhKvytYjW9vG/GKpnf+xP8UWgYEM/RaMzHHofbALDiKbbP1W8UEYmgGl39WkPZsg==
+
+just-diff-apply@^5.2.0:
+  version "5.5.0"
+  resolved "https://registry.yarnpkg.com/just-diff-apply/-/just-diff-apply-5.5.0.tgz#771c2ca9fa69f3d2b54e7c3f5c1dfcbcc47f9f0f"
+  integrity sha512-OYTthRfSh55WOItVqwpefPtNt2VdKsq5AnAK6apdtR6yCH8pr0CmSr710J0Mf+WdQy7K/OzMy7K2MgAfdQURDw==
+
+just-diff@^6.0.0:
+  version "6.0.2"
+  resolved "https://registry.yarnpkg.com/just-diff/-/just-diff-6.0.2.tgz#03b65908543ac0521caf6d8eb85035f7d27ea285"
+  integrity sha512-S59eriX5u3/QhMNq3v/gm8Kd0w8OS6Tz2FS1NG4blv+z0MuQcBRJyFWjdovM0Rad4/P4aUPFtnkNjMjyMlMSYA==
+
+keyv@^4.0.0:
+  version "4.5.4"
+  resolved "https://registry.yarnpkg.com/keyv/-/keyv-4.5.4.tgz#a879a99e29452f942439f2a405e3af8b31d4de93"
+  integrity sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==
+  dependencies:
+    json-buffer "3.0.1"
+
+locate-path@^7.1.0:
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/locate-path/-/locate-path-7.2.0.tgz#69cb1779bd90b35ab1e771e1f2f89a202c2a8a8a"
+  integrity sha512-gvVijfZvn7R+2qyPX8mAuKcFGDf6Nc61GdvGafQsHL0sBIxfKzA+usWn4GFC/bk+QdwPUD4kWFJLhElipq+0VA==
+  dependencies:
+    p-locate "^6.0.0"
+
+lodash.camelcase@^4.3.0:
+  version "4.3.0"
+  resolved "https://registry.yarnpkg.com/lodash.camelcase/-/lodash.camelcase-4.3.0.tgz#b28aa6288a2b9fc651035c7711f65ab6190331a6"
+  integrity sha512-TwuEnCnxbc3rAvhf/LbG7tJUDzhqXyFnv3dtzLOPgCG/hODL7WFnsbwktkD7yUV0RrreP/l1PALq/YSg6VvjlA==
+
+long@^5.0.0:
+  version "5.2.3"
+  resolved "https://registry.yarnpkg.com/long/-/long-5.2.3.tgz#a3ba97f3877cf1d778eccbcb048525ebb77499e1"
+  integrity sha512-lcHwpNoggQTObv5apGNCTdJrO69eHOZMi4BNC+rTLER8iHAqGrUVeLh/irVIM7zTw2bOXA8T6uNPeujwOLg/2Q==
+
+lowercase-keys@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/lowercase-keys/-/lowercase-keys-2.0.0.tgz#2603e78b7b4b0006cbca2fbcc8a3202558ac9479"
+  integrity sha512-tqNXrS78oMOE73NMxK4EMLQsQowWf8jKooH9g7xPavRT706R6bkQJ6DY2Te7QukaZsulxa30wQ7bk0pm4XiHmA==
+
+lru-cache@^10.0.1, "lru-cache@^9.1.1 || ^10.0.0":
+  version "10.2.0"
+  resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-10.2.0.tgz#0bd445ca57363465900f4d1f9bd8db343a4d95c3"
+  integrity sha512-2bIM8x+VAf6JT4bKAljS1qUWgMsqZRPGJS6FSahIMPVvctcNhyVp7AJu7quxOW9jwkryBReKZY5tY5JYv2n/7Q==
+
+lru-cache@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94"
+  integrity sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==
+  dependencies:
+    yallist "^4.0.0"
+
+make-error@^1.1.1:
+  version "1.3.6"
+  resolved "https://registry.yarnpkg.com/make-error/-/make-error-1.3.6.tgz#2eb2e37ea9b67c4891f684a1394799af484cf7a2"
+  integrity sha512-s8UhlNe7vPKomQhC1qFelMokr/Sc3AgNbso3n74mVPA5LTZwkB9NlXf4XPamLxJE8h0gh73rM94xvwRT2CVInw==
+
+make-fetch-happen@^13.0.0:
+  version "13.0.0"
+  resolved "https://registry.yarnpkg.com/make-fetch-happen/-/make-fetch-happen-13.0.0.tgz#705d6f6cbd7faecb8eac2432f551e49475bfedf0"
+  integrity sha512-7ThobcL8brtGo9CavByQrQi+23aIfgYU++wg4B87AIS8Rb2ZBt/MEaDqzA00Xwv/jUjAjYkLHjVolYuTLKda2A==
+  dependencies:
+    "@npmcli/agent" "^2.0.0"
+    cacache "^18.0.0"
+    http-cache-semantics "^4.1.1"
+    is-lambda "^1.0.1"
+    minipass "^7.0.2"
+    minipass-fetch "^3.0.0"
+    minipass-flush "^1.0.5"
+    minipass-pipeline "^1.2.4"
+    negotiator "^0.6.3"
+    promise-retry "^2.0.1"
+    ssri "^10.0.0"
+
+merge-stream@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/merge-stream/-/merge-stream-2.0.0.tgz#52823629a14dd00c9770fb6ad47dc6310f2c1f60"
+  integrity sha512-abv/qOcuPfk3URPfDzmZU1LKmuw8kT+0nIHvKrKgFrwifol/doWcdA4ZqsWQ8ENrFKkd67Mfpo/LovbIUsbt3w==
+
+mime@^2.0.0:
+  version "2.6.0"
+  resolved "https://registry.yarnpkg.com/mime/-/mime-2.6.0.tgz#a2a682a95cd4d0cb1d6257e28f83da7e35800367"
+  integrity sha512-USPkMeET31rOMiarsBNIHZKLGgvKc/LrjofAnBlOttf5ajRvqiRA8QsenbcooctK6d6Ts6aqZXBA+XbkKthiQg==
+
+mimic-fn@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/mimic-fn/-/mimic-fn-2.1.0.tgz#7ed2c2ccccaf84d3ffcb7a69b57711fc2083401b"
+  integrity sha512-OqbOk5oEQeAZ8WXWydlu9HJjz9WVdEIvamMCcXmuqUYjTknH/sqsWvhQ3vgwKFRR1HpjvNBKQ37nbJgYzGqGcg==
+
+mimic-response@^1.0.0:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-1.0.1.tgz#4923538878eef42063cb8a3e3b0798781487ab1b"
+  integrity sha512-j5EctnkH7amfV/q5Hgmoal1g2QHFJRraOtmx0JpIqkxhBhI/lJSl1nMpQ45hVarwNETOoWEimndZ4QK0RHxuxQ==
+
+mimic-response@^3.1.0:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/mimic-response/-/mimic-response-3.1.0.tgz#2d1d59af9c1b129815accc2c46a022a5ce1fa3c9"
+  integrity sha512-z0yWI+4FDrrweS8Zmt4Ej5HdJmky15+L2e6Wgn3+iK5fWzb6T3fhNFq2+MeTRb064c6Wr4N/wv0DzQTjNzHNGQ==
+
+minimatch@^9.0.0, minimatch@^9.0.1, minimatch@^9.0.3:
+  version "9.0.3"
+  resolved "https://registry.yarnpkg.com/minimatch/-/minimatch-9.0.3.tgz#a6e00c3de44c3a542bfaae70abfc22420a6da825"
+  integrity sha512-RHiac9mvaRw0x3AYRgDC1CxAP7HTcNrrECeA8YYJeWnpo+2Q5CegtZjaotWTWxDG3UeGA1coE05iH1mPjT/2mg==
+  dependencies:
+    brace-expansion "^2.0.1"
+
+minimist@^1.2.0, minimist@^1.2.6:
+  version "1.2.8"
+  resolved "https://registry.yarnpkg.com/minimist/-/minimist-1.2.8.tgz#c1a464e7693302e082a075cee0c057741ac4772c"
+  integrity sha512-2yyAR8qBkN3YuheJanUpWC5U3bb5osDywNB8RzDVlDwDHbocAJveqqj1u8+SVD7jkWT4yvsHCpWqqWqAxb0zCA==
+
+minipass-collect@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/minipass-collect/-/minipass-collect-2.0.1.tgz#1621bc77e12258a12c60d34e2276ec5c20680863"
+  integrity sha512-D7V8PO9oaz7PWGLbCACuI1qEOsq7UKfLotx/C0Aet43fCUB/wfQ7DYeq2oR/svFJGYDHPr38SHATeaj/ZoKHKw==
+  dependencies:
+    minipass "^7.0.3"
+
+minipass-fetch@^3.0.0:
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/minipass-fetch/-/minipass-fetch-3.0.4.tgz#4d4d9b9f34053af6c6e597a64be8e66e42bf45b7"
+  integrity sha512-jHAqnA728uUpIaFm7NWsCnqKT6UqZz7GcI/bDpPATuwYyKwJwW0remxSCxUlKiEty+eopHGa3oc8WxgQ1FFJqg==
+  dependencies:
+    minipass "^7.0.3"
+    minipass-sized "^1.0.3"
+    minizlib "^2.1.2"
+  optionalDependencies:
+    encoding "^0.1.13"
+
+minipass-flush@^1.0.5:
+  version "1.0.5"
+  resolved "https://registry.yarnpkg.com/minipass-flush/-/minipass-flush-1.0.5.tgz#82e7135d7e89a50ffe64610a787953c4c4cbb373"
+  integrity sha512-JmQSYYpPUqX5Jyn1mXaRwOda1uQ8HP5KAT/oDSLCzt1BYRhQU0/hDtsB1ufZfEEzMZ9aAVmsBw8+FWsIXlClWw==
+  dependencies:
+    minipass "^3.0.0"
+
+minipass-json-stream@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/minipass-json-stream/-/minipass-json-stream-1.0.1.tgz#7edbb92588fbfc2ff1db2fc10397acb7b6b44aa7"
+  integrity sha512-ODqY18UZt/I8k+b7rl2AENgbWE8IDYam+undIJONvigAz8KR5GWblsFTEfQs0WODsjbSXWlm+JHEv8Gr6Tfdbg==
+  dependencies:
+    jsonparse "^1.3.1"
+    minipass "^3.0.0"
+
+minipass-pipeline@^1.2.4:
+  version "1.2.4"
+  resolved "https://registry.yarnpkg.com/minipass-pipeline/-/minipass-pipeline-1.2.4.tgz#68472f79711c084657c067c5c6ad93cddea8214c"
+  integrity sha512-xuIq7cIOt09RPRJ19gdi4b+RiNvDFYe5JH+ggNvBqGqpQXcru3PcRmOZuHBKWK1Txf9+cQ+HMVN4d6z46LZP7A==
+  dependencies:
+    minipass "^3.0.0"
+
+minipass-sized@^1.0.3:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/minipass-sized/-/minipass-sized-1.0.3.tgz#70ee5a7c5052070afacfbc22977ea79def353b70"
+  integrity sha512-MbkQQ2CTiBMlA2Dm/5cY+9SWFEN8pzzOXi6rlM5Xxq0Yqbda5ZQy9sU75a673FE9ZK0Zsbr6Y5iP6u9nktfg2g==
+  dependencies:
+    minipass "^3.0.0"
+
+minipass@^3.0.0:
+  version "3.3.6"
+  resolved "https://registry.yarnpkg.com/minipass/-/minipass-3.3.6.tgz#7bba384db3a1520d18c9c0e5251c3444e95dd94a"
+  integrity sha512-DxiNidxSEK+tHG6zOIklvNOwm3hvCrbUrdtzY74U6HKTJxvIDfOUL5W5P2Ghd3DTkhhKPYGqeNUIh5qcM4YBfw==
+  dependencies:
+    yallist "^4.0.0"
+
+minipass@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/minipass/-/minipass-5.0.0.tgz#3e9788ffb90b694a5d0ec94479a45b5d8738133d"
+  integrity sha512-3FnjYuehv9k6ovOEbyOswadCDPX1piCfhV8ncmYtHOjuPwylVWsghTLo7rabjC3Rx5xD4HDx8Wm1xnMF7S5qFQ==
+
+"minipass@^5.0.0 || ^6.0.2 || ^7.0.0", minipass@^7.0.2, minipass@^7.0.3:
+  version "7.0.4"
+  resolved "https://registry.yarnpkg.com/minipass/-/minipass-7.0.4.tgz#dbce03740f50a4786ba994c1fb908844d27b038c"
+  integrity sha512-jYofLM5Dam9279rdkWzqHozUo4ybjdZmCsDHePy5V/PbBcVMiSZR97gmAy45aqi8CK1lG2ECd356FU86avfwUQ==
+
+minizlib@^2.1.1, minizlib@^2.1.2:
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-2.1.2.tgz#e90d3466ba209b932451508a11ce3d3632145931"
+  integrity sha512-bAxsR8BVfj60DWXHE3u30oHzfl4G7khkSuPW+qvpd7jFRHm7dLxOjUk1EHACJ/hxLY8phGJ0YhYHZo7jil7Qdg==
+  dependencies:
+    minipass "^3.0.0"
+    yallist "^4.0.0"
+
+mkdirp@^0.5.1:
+  version "0.5.6"
+  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.6.tgz#7def03d2432dcae4ba1d611445c48396062255f6"
+  integrity sha512-FP+p8RB8OWpF3YZBCrP5gtADmtXApB5AMLn+vdyA+PyxCjrCs00mjyUozssO33cwDeT3wNGdLxJ5M//YqtHAJw==
+  dependencies:
+    minimist "^1.2.6"
+
+mkdirp@^1.0.3:
+  version "1.0.4"
+  resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-1.0.4.tgz#3eb5ed62622756d79a5f0e2a221dfebad75c2f7e"
+  integrity sha512-vVqVZQyf3WLx2Shd0qJ9xuvqgAyKPLAiqITEtqW0oIUjzo3PePDd6fW9iFz30ef7Ysp/oiWqbhszeGWW2T6Gzw==
+
+module-details-from-path@^1.0.3:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/module-details-from-path/-/module-details-from-path-1.0.3.tgz#114c949673e2a8a35e9d35788527aa37b679da2b"
+  integrity sha512-ySViT69/76t8VhE1xXHK6Ch4NcDd26gx0MzKXLO+F7NOtnqH68d9zF94nT8ZWSxXh8ELOERsnJO/sWt1xZYw5A==
+
+ms@2.1.2:
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/ms/-/ms-2.1.2.tgz#d09d1f357b443f493382a8eb3ccd183872ae6009"
+  integrity sha512-sGkPx+VjMtmA6MX27oA4FBFELFCZZ4S4XqeGOXCv68tT+jb3vk/RyaKWP0PTKyWtmLSM0b+adUTEvbs1PEaH2w==
+
+negotiator@^0.6.3:
+  version "0.6.3"
+  resolved "https://registry.yarnpkg.com/negotiator/-/negotiator-0.6.3.tgz#58e323a72fedc0d6f9cd4d31fe49f51479590ccd"
+  integrity sha512-+EUsqGPLsM+j/zdChZjsnX51g4XrHFOIXwfnCVPGlQk/k5giakcKsuxCObBRu6DSm9opw/O6slWbJdghQM4bBg==
+
+node-gyp@^10.0.0:
+  version "10.0.1"
+  resolved "https://registry.yarnpkg.com/node-gyp/-/node-gyp-10.0.1.tgz#205514fc19e5830fa991e4a689f9e81af377a966"
+  integrity sha512-gg3/bHehQfZivQVfqIyy8wTdSymF9yTyP4CJifK73imyNMU8AIGQE2pUa7dNWfmMeG9cDVF2eehiRMv0LC1iAg==
+  dependencies:
+    env-paths "^2.2.0"
+    exponential-backoff "^3.1.1"
+    glob "^10.3.10"
+    graceful-fs "^4.2.6"
+    make-fetch-happen "^13.0.0"
+    nopt "^7.0.0"
+    proc-log "^3.0.0"
+    semver "^7.3.5"
+    tar "^6.1.2"
+    which "^4.0.0"
+
+nopt@^7.0.0:
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/nopt/-/nopt-7.2.0.tgz#067378c68116f602f552876194fd11f1292503d7"
+  integrity sha512-CVDtwCdhYIvnAzFoJ6NJ6dX3oga9/HyciQDnG1vQDjSLMeKLJ4A93ZqYKDrgYSr1FBY5/hMYC+2VCi24pgpkGA==
+  dependencies:
+    abbrev "^2.0.0"
+
+normalize-package-data@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/normalize-package-data/-/normalize-package-data-6.0.0.tgz#68a96b3c11edd462af7189c837b6b1064a484196"
+  integrity sha512-UL7ELRVxYBHBgYEtZCXjxuD5vPxnmvMGq0jp/dGPKKrN7tfsBh2IY7TlJ15WWwdjRWD3RJbnsygUurTK3xkPkg==
+  dependencies:
+    hosted-git-info "^7.0.0"
+    is-core-module "^2.8.1"
+    semver "^7.3.5"
+    validate-npm-package-license "^3.0.4"
+
+normalize-url@^6.0.1:
+  version "6.1.0"
+  resolved "https://registry.yarnpkg.com/normalize-url/-/normalize-url-6.1.0.tgz#40d0885b535deffe3f3147bec877d05fe4c5668a"
+  integrity sha512-DlL+XwOy3NxAQ8xuC0okPgK46iuVNAK01YN7RueYBqqFeGsBjV9XmCAzAdgt+667bCl5kPh9EqKKDwnaPG1I7A==
+
+npm-bundled@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-3.0.0.tgz#7e8e2f8bb26b794265028491be60321a25a39db7"
+  integrity sha512-Vq0eyEQy+elFpzsKjMss9kxqb9tG3YHg4dsyWuUENuzvSUWe1TCnW/vV9FkhvBk/brEDoDiVd+M1Btosa6ImdQ==
+  dependencies:
+    npm-normalize-package-bin "^3.0.0"
+
+npm-install-checks@^6.0.0, npm-install-checks@^6.2.0:
+  version "6.3.0"
+  resolved "https://registry.yarnpkg.com/npm-install-checks/-/npm-install-checks-6.3.0.tgz#046552d8920e801fa9f919cad569545d60e826fe"
+  integrity sha512-W29RiK/xtpCGqn6f3ixfRYGk+zRyr+Ew9F2E20BfXxT5/euLdA/Nm7fO7OeTGuAmTs30cpgInyJ0cYe708YTZw==
+  dependencies:
+    semver "^7.1.1"
+
+npm-normalize-package-bin@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/npm-normalize-package-bin/-/npm-normalize-package-bin-3.0.1.tgz#25447e32a9a7de1f51362c61a559233b89947832"
+  integrity sha512-dMxCf+zZ+3zeQZXKxmyuCKlIDPGuv8EF940xbkC4kQVDTtqoh6rJFO+JTKSA6/Rwi0getWmtuy4Itup0AMcaDQ==
+
+npm-package-arg@^11.0.0, npm-package-arg@^11.0.1:
+  version "11.0.1"
+  resolved "https://registry.yarnpkg.com/npm-package-arg/-/npm-package-arg-11.0.1.tgz#f208b0022c29240a1c532a449bdde3f0a4708ebc"
+  integrity sha512-M7s1BD4NxdAvBKUPqqRW957Xwcl/4Zvo8Aj+ANrzvIPzGJZElrH7Z//rSaec2ORcND6FHHLnZeY8qgTpXDMFQQ==
+  dependencies:
+    hosted-git-info "^7.0.0"
+    proc-log "^3.0.0"
+    semver "^7.3.5"
+    validate-npm-package-name "^5.0.0"
+
+npm-packlist@^8.0.0:
+  version "8.0.2"
+  resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-8.0.2.tgz#5b8d1d906d96d21c85ebbeed2cf54147477c8478"
+  integrity sha512-shYrPFIS/JLP4oQmAwDyk5HcyysKW8/JLTEA32S0Z5TzvpaeeX2yMFfoK1fjEBnCBvVyIB/Jj/GBFdm0wsgzbA==
+  dependencies:
+    ignore-walk "^6.0.4"
+
+npm-pick-manifest@^9.0.0:
+  version "9.0.0"
+  resolved "https://registry.yarnpkg.com/npm-pick-manifest/-/npm-pick-manifest-9.0.0.tgz#f87a4c134504a2c7931f2bb8733126e3c3bb7e8f"
+  integrity sha512-VfvRSs/b6n9ol4Qb+bDwNGUXutpy76x6MARw/XssevE0TnctIKcmklJZM5Z7nqs5z5aW+0S63pgCNbpkUNNXBg==
+  dependencies:
+    npm-install-checks "^6.0.0"
+    npm-normalize-package-bin "^3.0.0"
+    npm-package-arg "^11.0.0"
+    semver "^7.3.5"
+
+npm-registry-fetch@^16.0.0:
+  version "16.1.0"
+  resolved "https://registry.yarnpkg.com/npm-registry-fetch/-/npm-registry-fetch-16.1.0.tgz#10227b7b36c97bc1cf2902a24e4f710cfe62803c"
+  integrity sha512-PQCELXKt8Azvxnt5Y85GseQDJJlglTFM9L9U9gkv2y4e9s0k3GVDdOx3YoB6gm2Do0hlkzC39iCGXby+Wve1Bw==
+  dependencies:
+    make-fetch-happen "^13.0.0"
+    minipass "^7.0.2"
+    minipass-fetch "^3.0.0"
+    minipass-json-stream "^1.0.1"
+    minizlib "^2.1.2"
+    npm-package-arg "^11.0.0"
+    proc-log "^3.0.0"
+
+npm-run-path@^4.0.1:
+  version "4.0.1"
+  resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-4.0.1.tgz#b7ecd1e5ed53da8e37a55e1c2269e0b97ed748ea"
+  integrity sha512-S48WzZW777zhNIrn7gxOlISNAqi9ZC/uQFnRdbeIHhZhCA6UqpkOT8T1G7BvfdgP4Er8gF4sUbaS0i7QvIfCWw==
+  dependencies:
+    path-key "^3.0.0"
+
+npmlog@^7.0.1:
+  version "7.0.1"
+  resolved "https://registry.yarnpkg.com/npmlog/-/npmlog-7.0.1.tgz#7372151a01ccb095c47d8bf1d0771a4ff1f53ac8"
+  integrity sha512-uJ0YFk/mCQpLBt+bxN88AKd+gyqZvZDbtiNxk6Waqcj2aPRyfVx8ITawkyQynxUagInjdYT1+qj4NfA5KJJUxg==
+  dependencies:
+    are-we-there-yet "^4.0.0"
+    console-control-strings "^1.1.0"
+    gauge "^5.0.0"
+    set-blocking "^2.0.0"
+
+once@^1.3.1, once@^1.4.0:
+  version "1.4.0"
+  resolved "https://registry.yarnpkg.com/once/-/once-1.4.0.tgz#583b1aa775961d4b113ac17d9c50baef9dd76bd1"
+  integrity sha512-lNaJgI+2Q5URQBkccEKHTQOPaXdUxnZZElQTZY0MFUAuaEqe1E+Nyvgdz/aIyNi6Z9MzO5dv1H8n58/GELp3+w==
+  dependencies:
+    wrappy "1"
+
+onetime@^5.1.2:
+  version "5.1.2"
+  resolved "https://registry.yarnpkg.com/onetime/-/onetime-5.1.2.tgz#d0e96ebb56b07476df1dd9c4806e5237985ca45e"
+  integrity sha512-kbpaSSGJTWdAY5KPVeMOKXSrPtr8C8C7wodJbcsd51jRnmD+GZu8Y0VoU6Dm5Z4vWr0Ig/1NKuWRKf7j5aaYSg==
+  dependencies:
+    mimic-fn "^2.1.0"
+
+p-cancelable@^2.0.0:
+  version "2.1.1"
+  resolved "https://registry.yarnpkg.com/p-cancelable/-/p-cancelable-2.1.1.tgz#aab7fbd416582fa32a3db49859c122487c5ed2cf"
+  integrity sha512-BZOr3nRQHOntUjTrH8+Lh54smKHoHyur8We1V8DSMVrl5A2malOOwuJRnKRDjSnkoeBh4at6BwEnb5I7Jl31wg==
+
+p-limit@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/p-limit/-/p-limit-4.0.0.tgz#914af6544ed32bfa54670b061cafcbd04984b644"
+  integrity sha512-5b0R4txpzjPWVw/cXXUResoD4hb6U/x9BH08L7nw+GN1sezDzPdxeRvpc9c433fZhBan/wusjbCsqwqm4EIBIQ==
+  dependencies:
+    yocto-queue "^1.0.0"
+
+p-locate@^6.0.0:
+  version "6.0.0"
+  resolved "https://registry.yarnpkg.com/p-locate/-/p-locate-6.0.0.tgz#3da9a49d4934b901089dca3302fa65dc5a05c04f"
+  integrity sha512-wPrq66Llhl7/4AGC6I+cqxT07LhXvWL08LNXz1fENOw0Ap4sRZZ/gZpTTJ5jpurzzzfS2W/Ge9BY3LgLjCShcw==
+  dependencies:
+    p-limit "^4.0.0"
+
+p-map@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/p-map/-/p-map-4.0.0.tgz#bb2f95a5eda2ec168ec9274e06a747c3e2904d2b"
+  integrity sha512-/bjOqmgETBYB5BoEeGVea8dmvHb2m9GLy1E9W43yeyfP6QQCZGFNa+XRceJEuDB6zqr+gKpIAmlLebMpykw/MQ==
+  dependencies:
+    aggregate-error "^3.0.0"
+
+pacote@^17.0.0, pacote@^17.0.4:
+  version "17.0.6"
+  resolved "https://registry.yarnpkg.com/pacote/-/pacote-17.0.6.tgz#874bb59cda5d44ab784d0b6530fcb4a7d9b76a60"
+  integrity sha512-cJKrW21VRE8vVTRskJo78c/RCvwJCn1f4qgfxL4w77SOWrTCRcmfkYHlHtS0gqpgjv3zhXflRtgsrUCX5xwNnQ==
+  dependencies:
+    "@npmcli/git" "^5.0.0"
+    "@npmcli/installed-package-contents" "^2.0.1"
+    "@npmcli/promise-spawn" "^7.0.0"
+    "@npmcli/run-script" "^7.0.0"
+    cacache "^18.0.0"
+    fs-minipass "^3.0.0"
+    minipass "^7.0.2"
+    npm-package-arg "^11.0.0"
+    npm-packlist "^8.0.0"
+    npm-pick-manifest "^9.0.0"
+    npm-registry-fetch "^16.0.0"
+    proc-log "^3.0.0"
+    promise-retry "^2.0.1"
+    read-package-json "^7.0.0"
+    read-package-json-fast "^3.0.0"
+    sigstore "^2.2.0"
+    ssri "^10.0.0"
+    tar "^6.1.11"
+
+parse-conflict-json@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/parse-conflict-json/-/parse-conflict-json-3.0.1.tgz#67dc55312781e62aa2ddb91452c7606d1969960c"
+  integrity sha512-01TvEktc68vwbJOtWZluyWeVGWjP+bZwXtPDMQVbBKzbJ/vZBif0L69KH1+cHv1SZ6e0FKLvjyHe8mqsIqYOmw==
+  dependencies:
+    json-parse-even-better-errors "^3.0.0"
+    just-diff "^6.0.0"
+    just-diff-apply "^5.2.0"
+
+path-exists@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/path-exists/-/path-exists-5.0.0.tgz#a6aad9489200b21fab31e49cf09277e5116fb9e7"
+  integrity sha512-RjhtfwJOxzcFmNOi6ltcbcu4Iu+FL3zEj83dk4kAS+fVpTxXLO1b38RvJgT/0QwvV/L3aY9TAnyv0EOqW4GoMQ==
+
+path-key@^3.0.0, path-key@^3.1.0:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/path-key/-/path-key-3.1.1.tgz#581f6ade658cbba65a0d3380de7753295054f375"
+  integrity sha512-ojmeN0qd+y0jszEtoY48r0Peq5dwMEkIlCOu6Q5f41lfkswXuKtYrhgoTpLnyIcHm24Uhqx+5Tqm2InSwLhE6Q==
+
+path-parse@^1.0.7:
+  version "1.0.7"
+  resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
+  integrity sha512-LDJzPVEEEPR+y48z93A0Ed0yXb8pAByGWo/k5YYdYgpY2/2EsOsksJrq7lOHxryrVOn1ejG6oAp8ahvOIQD8sw==
+
+path-scurry@^1.10.1:
+  version "1.10.1"
+  resolved "https://registry.yarnpkg.com/path-scurry/-/path-scurry-1.10.1.tgz#9ba6bf5aa8500fe9fd67df4f0d9483b2b0bfc698"
+  integrity sha512-MkhCqzzBEpPvxxQ71Md0b1Kk51W01lrYvlMzSUaIzNsODdd7mqhiimSZlr+VegAz5Z6Vzt9Xg2ttE//XBhH3EQ==
+  dependencies:
+    lru-cache "^9.1.1 || ^10.0.0"
+    minipass "^5.0.0 || ^6.0.2 || ^7.0.0"
+
+picomatch@^3.0.1:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/picomatch/-/picomatch-3.0.1.tgz#817033161def55ec9638567a2f3bbc876b3e7516"
+  integrity sha512-I3EurrIQMlRc9IaAZnqRR044Phh2DXY+55o7uJ0V+hYZAcQYSuFWsc9q5PvyDHUSCe1Qxn/iBz+78s86zWnGag==
+
+pkg-dir@^7.0.0:
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/pkg-dir/-/pkg-dir-7.0.0.tgz#8f0c08d6df4476756c5ff29b3282d0bab7517d11"
+  integrity sha512-Ie9z/WINcxxLp27BKOCHGde4ITq9UklYKDzVo1nhk5sqGEXU3FpkwP5GM2voTGJkGd9B3Otl+Q4uwSOeSUtOBA==
+  dependencies:
+    find-up "^6.3.0"
+
+possible-typed-array-names@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/possible-typed-array-names/-/possible-typed-array-names-1.0.0.tgz#89bb63c6fada2c3e90adc4a647beeeb39cc7bf8f"
+  integrity sha512-d7Uw+eZoloe0EHDIYoe+bQ5WXnGMOpmiZFTuMWCwpjzzkL2nTjcKiAk4hh8TjnGye2TwWOk3UXucZ+3rbmBa8Q==
+
+postcss-selector-parser@^6.0.10:
+  version "6.0.16"
+  resolved "https://registry.yarnpkg.com/postcss-selector-parser/-/postcss-selector-parser-6.0.16.tgz#3b88b9f5c5abd989ef4e2fc9ec8eedd34b20fb04"
+  integrity sha512-A0RVJrX+IUkVZbW3ClroRWurercFhieevHB38sr2+l9eUClMqome3LmEmnhlNy+5Mr2EYN6B2Kaw9wYdd+VHiw==
+  dependencies:
+    cssesc "^3.0.0"
+    util-deprecate "^1.0.2"
+
+proc-log@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/proc-log/-/proc-log-3.0.0.tgz#fb05ef83ccd64fd7b20bbe9c8c1070fc08338dd8"
+  integrity sha512-++Vn7NS4Xf9NacaU9Xq3URUuqZETPsf8L4j5/ckhaRYsfPeRyzGw+iDjFhV/Jr3uNmTvvddEJFWh5R1gRgUH8A==
+
+promise-all-reject-late@^1.0.0:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/promise-all-reject-late/-/promise-all-reject-late-1.0.1.tgz#f8ebf13483e5ca91ad809ccc2fcf25f26f8643c2"
+  integrity sha512-vuf0Lf0lOxyQREH7GDIOUMLS7kz+gs8i6B+Yi8dC68a2sychGrHTJYghMBD6k7eUcH0H5P73EckCA48xijWqXw==
+
+promise-call-limit@^3.0.1:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/promise-call-limit/-/promise-call-limit-3.0.1.tgz#3570f7a3f2aaaf8e703623a552cd74749688cf19"
+  integrity sha512-utl+0x8gIDasV5X+PI5qWEPqH6fJS0pFtQ/4gZ95xfEFb/89dmh+/b895TbFDBLiafBvxD/PGTKfvxl4kH/pQg==
+
+promise-inflight@^1.0.1:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/promise-inflight/-/promise-inflight-1.0.1.tgz#98472870bf228132fcbdd868129bad12c3c029e3"
+  integrity sha512-6zWPyEOFaQBJYcGMHBKTKJ3u6TBsnMFOIZSa6ce1e/ZrrsOlnHRHbabMjLiBYKp+n44X9eUI6VUPaukCXHuG4g==
+
+promise-retry@^2.0.1:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/promise-retry/-/promise-retry-2.0.1.tgz#ff747a13620ab57ba688f5fc67855410c370da22"
+  integrity sha512-y+WKFlBR8BGXnsNlIHFGPZmyDf3DFMoLhaflAnyZgV6rG6xu+JwesTo2Q9R6XwYmtmwAFCkAk3e35jEdoeh/3g==
+  dependencies:
+    err-code "^2.0.2"
+    retry "^0.12.0"
+
+protobufjs@^7.2.4:
+  version "7.2.6"
+  resolved "https://registry.yarnpkg.com/protobufjs/-/protobufjs-7.2.6.tgz#4a0ccd79eb292717aacf07530a07e0ed20278215"
+  integrity sha512-dgJaEDDL6x8ASUZ1YqWciTRrdOuYNzoOf27oHNfdyvKqHr5i0FV7FSLU+aIeFjyFgVxrpTOtQUi0BLLBymZaBw==
+  dependencies:
+    "@protobufjs/aspromise" "^1.1.2"
+    "@protobufjs/base64" "^1.1.2"
+    "@protobufjs/codegen" "^2.0.4"
+    "@protobufjs/eventemitter" "^1.1.0"
+    "@protobufjs/fetch" "^1.1.0"
+    "@protobufjs/float" "^1.0.2"
+    "@protobufjs/inquire" "^1.1.0"
+    "@protobufjs/path" "^1.1.2"
+    "@protobufjs/pool" "^1.1.0"
+    "@protobufjs/utf8" "^1.1.0"
+    "@types/node" ">=13.7.0"
+    long "^5.0.0"
+
+pump@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/pump/-/pump-3.0.0.tgz#b4a2116815bde2f4e1ea602354e8c75565107a64"
+  integrity sha512-LwZy+p3SFs1Pytd/jYct4wpv49HiYCqd9Rlc5ZVdk0V+8Yzv6jR5Blk3TRmPL1ft69TxP0IMZGJ+WPFU2BFhww==
+  dependencies:
+    end-of-stream "^1.1.0"
+    once "^1.3.1"
+
+punycode@1.3.2:
+  version "1.3.2"
+  resolved "https://registry.yarnpkg.com/punycode/-/punycode-1.3.2.tgz#9653a036fb7c1ee42342f2325cceefea3926c48d"
+  integrity sha512-RofWgt/7fL5wP1Y7fxE7/EmTLzQVnB0ycyibJ0OOHIlJqTNzglYFxVwETOcIoJqJmpDXJ9xImDv+Fq34F/d4Dw==
+
+querystring@0.2.0:
+  version "0.2.0"
+  resolved "https://registry.yarnpkg.com/querystring/-/querystring-0.2.0.tgz#b209849203bb25df820da756e747005878521620"
+  integrity sha512-X/xY82scca2tau62i9mDyU9K+I+djTMUsvwf7xnUX5GLvVzgJybOJf4Y6o9Zx3oJK/LSXg5tTZBjwzqVPaPO2g==
+
+quick-lru@^5.1.1:
+  version "5.1.1"
+  resolved "https://registry.yarnpkg.com/quick-lru/-/quick-lru-5.1.1.tgz#366493e6b3e42a3a6885e2e99d18f80fb7a8c932"
+  integrity sha512-WuyALRjWPDGtt/wzJiadO5AXY+8hZ80hVpe6MyivgraREW751X3SbhRvG3eLKOYN+8VEvqLcf3wdnt44Z4S4SA==
+
+read-cmd-shim@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/read-cmd-shim/-/read-cmd-shim-4.0.0.tgz#640a08b473a49043e394ae0c7a34dd822c73b9bb"
+  integrity sha512-yILWifhaSEEytfXI76kB9xEEiG1AiozaCJZ83A87ytjRiN+jVibXjedjCRNjoZviinhG+4UkalO3mWTd8u5O0Q==
+
+read-package-json-fast@^3.0.0, read-package-json-fast@^3.0.2:
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/read-package-json-fast/-/read-package-json-fast-3.0.2.tgz#394908a9725dc7a5f14e70c8e7556dff1d2b1049"
+  integrity sha512-0J+Msgym3vrLOUB3hzQCuZHII0xkNGCtz/HJH9xZshwv9DbDwkw1KaE3gx/e2J5rpEY5rtOy6cyhKOPrkP7FZw==
+  dependencies:
+    json-parse-even-better-errors "^3.0.0"
+    npm-normalize-package-bin "^3.0.0"
+
+read-package-json@^7.0.0:
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/read-package-json/-/read-package-json-7.0.0.tgz#d605c9dcf6bc5856da24204aa4e9518ee9714be0"
+  integrity sha512-uL4Z10OKV4p6vbdvIXB+OzhInYtIozl/VxUBPgNkBuUi2DeRonnuspmaVAMcrkmfjKGNmRndyQAbE7/AmzGwFg==
+  dependencies:
+    glob "^10.2.2"
+    json-parse-even-better-errors "^3.0.0"
+    normalize-package-data "^6.0.0"
+    npm-normalize-package-bin "^3.0.0"
+
+require-directory@^2.1.1:
+  version "2.1.1"
+  resolved "https://registry.yarnpkg.com/require-directory/-/require-directory-2.1.1.tgz#8c64ad5fd30dab1c976e2344ffe7f792a6a6df42"
+  integrity sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q==
+
+require-from-string@^2.0.1:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/require-from-string/-/require-from-string-2.0.2.tgz#89a7fdd938261267318eafe14f9c32e598c36909"
+  integrity sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw==
+
+require-in-the-middle@^5.0.3:
+  version "5.2.0"
+  resolved "https://registry.yarnpkg.com/require-in-the-middle/-/require-in-the-middle-5.2.0.tgz#4b71e3cc7f59977100af9beb76bf2d056a5a6de2"
+  integrity sha512-efCx3b+0Z69/LGJmm9Yvi4cqEdxnoGnxYxGxBghkkTTFeXRtTCmmhO0AnAfHz59k957uTSuy8WaHqOs8wbYUWg==
+  dependencies:
+    debug "^4.1.1"
+    module-details-from-path "^1.0.3"
+    resolve "^1.22.1"
+
+resolve-alpn@^1.0.0:
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/resolve-alpn/-/resolve-alpn-1.2.1.tgz#b7adbdac3546aaaec20b45e7d8265927072726f9"
+  integrity sha512-0a1F4l73/ZFZOakJnQ3FvkJ2+gSTQWz/r2KE5OdDY0TxPm5h4GkqkWWfM47T7HsbnOtcJVEF4epCVy6u7Q3K+g==
+
+resolve@^1.22.1, resolve@^1.7.1:
+  version "1.22.8"
+  resolved "https://registry.yarnpkg.com/resolve/-/resolve-1.22.8.tgz#b6c87a9f2aa06dfab52e3d70ac8cde321fa5a48d"
+  integrity sha512-oKWePCxqpd6FlLvGV1VU0x7bkPmmCNolxzjMf4NczoDnQcIWrAF+cPtZn5i6n+RfD2d9i0tzpKnG6Yk168yIyw==
+  dependencies:
+    is-core-module "^2.13.0"
+    path-parse "^1.0.7"
+    supports-preserve-symlinks-flag "^1.0.0"
+
+responselike@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/responselike/-/responselike-2.0.1.tgz#9a0bc8fdc252f3fb1cca68b016591059ba1422bc"
+  integrity sha512-4gl03wn3hj1HP3yzgdI7d3lCkF95F21Pz4BPGvKHinyQzALR5CapwC8yIi0Rh58DEMQ/SguC03wFj2k0M/mHhw==
+  dependencies:
+    lowercase-keys "^2.0.0"
+
+retry@^0.12.0:
+  version "0.12.0"
+  resolved "https://registry.yarnpkg.com/retry/-/retry-0.12.0.tgz#1b42a6266a21f07421d1b0b54b7dc167b01c013b"
+  integrity sha512-9LkiTwjUh6rT555DtE9rTX+BKByPfrMzEAtnlEtdEwr3Nkffwiihqe2bWADg+OQRjt9gl6ICdmB/ZFDCGAtSow==
+
+"safer-buffer@>= 2.1.2 < 3.0.0":
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a"
+  integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==
+
+sax@1.2.1:
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/sax/-/sax-1.2.1.tgz#7b8e656190b228e81a66aea748480d828cd2d37a"
+  integrity sha512-8I2a3LovHTOpm7NV5yOyO8IHqgVsfK4+UuySrXU8YXkSRX7k6hCV9b3HrkKCr3nMpgj+0bmocaJJWpvp1oc7ZA==
+
+sax@>=0.6.0:
+  version "1.3.0"
+  resolved "https://registry.yarnpkg.com/sax/-/sax-1.3.0.tgz#a5dbe77db3be05c9d1ee7785dbd3ea9de51593d0"
+  integrity sha512-0s+oAmw9zLl1V1cS9BtZN7JAd0cW5e0QH4W3LWEK6a4LaLEA2OTpGYWDY+6XasBLtz6wkm3u1xRw95mRuJ59WA==
+
+semver@^5.4.0:
+  version "5.7.2"
+  resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.2.tgz#48d55db737c3287cd4835e17fa13feace1c41ef8"
+  integrity sha512-cBznnQ9KjJqU67B52RMC65CMarK2600WFnbkcaiwWq3xy/5haFJlshgnpjovMVJ+Hff49d8GEn0b87C5pDQ10g==
+
+semver@^7.0.0, semver@^7.1.1, semver@^7.3.2, semver@^7.3.5, semver@^7.3.7, semver@^7.5.2, semver@^7.5.3:
+  version "7.6.0"
+  resolved "https://registry.yarnpkg.com/semver/-/semver-7.6.0.tgz#1a46a4db4bffcccd97b743b5005c8325f23d4e2d"
+  integrity sha512-EnwXhrlwXMk9gKu5/flx5sv/an57AkRplG3hTK68W7FRDN+k+OWBj65M7719OkA82XLBxrcX0KSHj+X5COhOVg==
+  dependencies:
+    lru-cache "^6.0.0"
+
+set-blocking@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/set-blocking/-/set-blocking-2.0.0.tgz#045f9782d011ae9a6803ddd382b24392b3d890f7"
+  integrity sha512-KiKBS8AnWGEyLzofFfmvKwpdPzqiy16LvQfK3yv/fVH7Bj13/wl3JSR1J+rfgRE9q7xUJK4qvgS8raSOeLUehw==
+
+set-function-length@^1.2.1:
+  version "1.2.2"
+  resolved "https://registry.yarnpkg.com/set-function-length/-/set-function-length-1.2.2.tgz#aac72314198eaed975cf77b2c3b6b880695e5449"
+  integrity sha512-pgRc4hJ4/sNjWCSS9AmnS40x3bNMDTknHgL5UaMBTMyJnU90EgWh1Rz+MC9eFu4BuN/UwZjKQuY/1v3rM7HMfg==
+  dependencies:
+    define-data-property "^1.1.4"
+    es-errors "^1.3.0"
+    function-bind "^1.1.2"
+    get-intrinsic "^1.2.4"
+    gopd "^1.0.1"
+    has-property-descriptors "^1.0.2"
+
+shebang-command@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/shebang-command/-/shebang-command-2.0.0.tgz#ccd0af4f8835fbdc265b82461aaf0c36663f34ea"
+  integrity sha512-kHxr2zZpYtdmrN1qDjrrX/Z1rR1kG8Dx+gkpK1G4eXmvXswmcE1hTWBWYUzlraYw1/yZp6YuDY77YtvbN0dmDA==
+  dependencies:
+    shebang-regex "^3.0.0"
+
+shebang-regex@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/shebang-regex/-/shebang-regex-3.0.0.tgz#ae16f1644d873ecad843b0307b143362d4c42172"
+  integrity sha512-7++dFhtcx3353uBaq8DDR4NuxBetBzC7ZQOhmTQInHEd6bSrXdiEyzCvG07Z44UYdLShWUyXt5M/yhz8ekcb1A==
+
+shimmer@^1.2.1:
+  version "1.2.1"
+  resolved "https://registry.yarnpkg.com/shimmer/-/shimmer-1.2.1.tgz#610859f7de327b587efebf501fb43117f9aff337"
+  integrity sha512-sQTKC1Re/rM6XyFM6fIAGHRPVGvyXfgzIDvzoq608vM+jeyVD0Tu1E6Np0Kc2zAIFWIj963V2800iF/9LPieQw==
+
+signal-exit@^3.0.3:
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-3.0.7.tgz#a9a1767f8af84155114eaabd73f99273c8f59ad9"
+  integrity sha512-wnD2ZE+l+SPC/uoS0vXeE9L1+0wuaMqKlfz9AMUo38JsyLSBWSFcHR1Rri62LZc12vLr1gb3jl7iwQhgwpAbGQ==
+
+signal-exit@^4.0.1:
+  version "4.1.0"
+  resolved "https://registry.yarnpkg.com/signal-exit/-/signal-exit-4.1.0.tgz#952188c1cbd546070e2dd20d0f41c0ae0530cb04"
+  integrity sha512-bzyZ1e88w9O1iNJbKnOlvYTrWPDl46O1bG0D3XInv+9tkPrxrN8jUUTiFlDkkmKWgn1M6CfIA13SuGqOa9Korw==
+
+sigstore@^2.2.0:
+  version "2.2.2"
+  resolved "https://registry.yarnpkg.com/sigstore/-/sigstore-2.2.2.tgz#5e4ff39febeae9e0679bafa22180cb0f445a7e35"
+  integrity sha512-2A3WvXkQurhuMgORgT60r6pOWiCOO5LlEqY2ADxGBDGVYLSo5HN0uLtb68YpVpuL/Vi8mLTe7+0Dx2Fq8lLqEg==
+  dependencies:
+    "@sigstore/bundle" "^2.2.0"
+    "@sigstore/core" "^1.0.0"
+    "@sigstore/protobuf-specs" "^0.3.0"
+    "@sigstore/sign" "^2.2.3"
+    "@sigstore/tuf" "^2.3.1"
+    "@sigstore/verify" "^1.1.0"
+
+smart-buffer@^4.2.0:
+  version "4.2.0"
+  resolved "https://registry.yarnpkg.com/smart-buffer/-/smart-buffer-4.2.0.tgz#6e1d71fa4f18c05f7d0ff216dd16a481d0e8d9ae"
+  integrity sha512-94hK0Hh8rPqQl2xXc3HsaBoOXKV20MToPkcXvwbISWLEs+64sBq5kFgn2kJDHb1Pry9yrP0dxrCI9RRci7RXKg==
+
+socks-proxy-agent@^8.0.1:
+  version "8.0.2"
+  resolved "https://registry.yarnpkg.com/socks-proxy-agent/-/socks-proxy-agent-8.0.2.tgz#5acbd7be7baf18c46a3f293a840109a430a640ad"
+  integrity sha512-8zuqoLv1aP/66PHF5TqwJ7Czm3Yv32urJQHrVyhD7mmA6d61Zv8cIXQYPTWwmg6qlupnPvs/QKDmfa4P/qct2g==
+  dependencies:
+    agent-base "^7.0.2"
+    debug "^4.3.4"
+    socks "^2.7.1"
+
+socks@^2.7.1:
+  version "2.8.1"
+  resolved "https://registry.yarnpkg.com/socks/-/socks-2.8.1.tgz#22c7d9dd7882649043cba0eafb49ae144e3457af"
+  integrity sha512-B6w7tkwNid7ToxjZ08rQMT8M9BJAf8DKx8Ft4NivzH0zBUfd6jldGcisJn/RLgxcX3FPNDdNQCUEMMT79b+oCQ==
+  dependencies:
+    ip-address "^9.0.5"
+    smart-buffer "^4.2.0"
+
+source-map-support@^0.5.6:
+  version "0.5.21"
+  resolved "https://registry.yarnpkg.com/source-map-support/-/source-map-support-0.5.21.tgz#04fe7c7f9e1ed2d662233c28cb2b35b9f63f6e4f"
+  integrity sha512-uBHU3L3czsIyYXKX88fdrGovxdSCoTGDRZ6SYXtSRxLZUzHg5P/66Ht6uoUlHu9EZod+inXhKo3qQgwXUT/y1w==
+  dependencies:
+    buffer-from "^1.0.0"
+    source-map "^0.6.0"
+
+source-map@^0.6.0:
+  version "0.6.1"
+  resolved "https://registry.yarnpkg.com/source-map/-/source-map-0.6.1.tgz#74722af32e9614e9c287a8d0bbde48b5e2f1a263"
+  integrity sha512-UjgapumWlbMhkBgzT7Ykc5YXUT46F0iKu8SGXq0bcwP5dz/h0Plj6enJqjz1Zbq2l5WaqYnrVbwWOWMyF3F47g==
+
+spdx-correct@^3.0.0:
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/spdx-correct/-/spdx-correct-3.2.0.tgz#4f5ab0668f0059e34f9c00dce331784a12de4e9c"
+  integrity sha512-kN9dJbvnySHULIluDHy32WHRUu3Og7B9sbY7tsFLctQkIqnMh3hErYgdMjTYuqmcXX+lK5T1lnUt3G7zNswmZA==
+  dependencies:
+    spdx-expression-parse "^3.0.0"
+    spdx-license-ids "^3.0.0"
+
+spdx-exceptions@^2.1.0:
+  version "2.5.0"
+  resolved "https://registry.yarnpkg.com/spdx-exceptions/-/spdx-exceptions-2.5.0.tgz#5d607d27fc806f66d7b64a766650fa890f04ed66"
+  integrity sha512-PiU42r+xO4UbUS1buo3LPJkjlO7430Xn5SVAhdpzzsPHsjbYVflnnFdATgabnLude+Cqu25p6N+g2lw/PFsa4w==
+
+spdx-expression-parse@^3.0.0:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/spdx-expression-parse/-/spdx-expression-parse-3.0.1.tgz#cf70f50482eefdc98e3ce0a6833e4a53ceeba679"
+  integrity sha512-cbqHunsQWnJNE6KhVSMsMeH5H/L9EpymbzqTQ3uLwNCLZ1Q481oWaofqH7nO6V07xlXwY6PhQdQ2IedWx/ZK4Q==
+  dependencies:
+    spdx-exceptions "^2.1.0"
+    spdx-license-ids "^3.0.0"
+
+spdx-license-ids@^3.0.0:
+  version "3.0.17"
+  resolved "https://registry.yarnpkg.com/spdx-license-ids/-/spdx-license-ids-3.0.17.tgz#887da8aa73218e51a1d917502d79863161a93f9c"
+  integrity sha512-sh8PWc/ftMqAAdFiBu6Fy6JUOYjqDJBJvIhpfDMyHrr0Rbp5liZqd4TjtQ/RgfLjKFZb+LMx5hpml5qOWy0qvg==
+
+sprintf-js@^1.1.3:
+  version "1.1.3"
+  resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.1.3.tgz#4914b903a2f8b685d17fdf78a70e917e872e444a"
+  integrity sha512-Oo+0REFV59/rz3gfJNKQiBlwfHaSESl1pcGyABQsnnIfWOFt6JNj5gCog2U6MLZ//IGYD+nA8nI+mTShREReaA==
+
+sprintf-js@~1.0.2:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.0.3.tgz#04e6926f662895354f3dd015203633b857297e2c"
+  integrity sha512-D9cPgkvLlV3t3IzL0D0YLvGA9Ahk4PcvVwUbN0dSGr1aP0Nrt4AEnTUbuGvquEC0mA64Gqt1fzirlRs5ibXx8g==
+
+ssri@^10.0.0, ssri@^10.0.5:
+  version "10.0.5"
+  resolved "https://registry.yarnpkg.com/ssri/-/ssri-10.0.5.tgz#e49efcd6e36385196cb515d3a2ad6c3f0265ef8c"
+  integrity sha512-bSf16tAFkGeRlUNDjXu8FzaMQt6g2HZJrun7mtMbIPOddxt3GLMSz5VWUWcqTJUPfLEaDIepGxv+bYQW49596A==
+  dependencies:
+    minipass "^7.0.3"
+
+"string-width-cjs@npm:string-width@^4.2.0", "string-width@^1.0.2 || 2 || 3 || 4", string-width@^4.1.0, string-width@^4.2.0, string-width@^4.2.3:
+  version "4.2.3"
+  resolved "https://registry.yarnpkg.com/string-width/-/string-width-4.2.3.tgz#269c7117d27b05ad2e536830a8ec895ef9c6d010"
+  integrity sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==
+  dependencies:
+    emoji-regex "^8.0.0"
+    is-fullwidth-code-point "^3.0.0"
+    strip-ansi "^6.0.1"
+
+string-width@^5.0.1, string-width@^5.1.2:
+  version "5.1.2"
+  resolved "https://registry.yarnpkg.com/string-width/-/string-width-5.1.2.tgz#14f8daec6d81e7221d2a357e668cab73bdbca794"
+  integrity sha512-HnLOCR3vjcY8beoNLtcjZ5/nxn2afmME6lhrDrebokqMap+XbeW8n9TXpPDOqdGK5qcI3oT0GKTW6wC7EMiVqA==
+  dependencies:
+    eastasianwidth "^0.2.0"
+    emoji-regex "^9.2.2"
+    strip-ansi "^7.0.1"
+
+"strip-ansi-cjs@npm:strip-ansi@^6.0.1", strip-ansi@^6.0.0, strip-ansi@^6.0.1:
+  version "6.0.1"
+  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-6.0.1.tgz#9e26c63d30f53443e9489495b2105d37b67a85d9"
+  integrity sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==
+  dependencies:
+    ansi-regex "^5.0.1"
+
+strip-ansi@^7.0.1:
+  version "7.1.0"
+  resolved "https://registry.yarnpkg.com/strip-ansi/-/strip-ansi-7.1.0.tgz#d5b6568ca689d8561370b0707685d22434faff45"
+  integrity sha512-iq6eVVI64nQQTRYq2KtEg2d2uU7LElhTJwsH4YzIHZshxlgZms/wIc4VoDQTlG/IvVIrBKG06CrZnp0qv7hkcQ==
+  dependencies:
+    ansi-regex "^6.0.1"
+
+strip-final-newline@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/strip-final-newline/-/strip-final-newline-2.0.0.tgz#89b852fb2fcbe936f6f4b3187afb0a12c1ab58ad"
+  integrity sha512-BrpvfNAE3dcvq7ll3xVumzjKjZQ5tI1sEUIKr3Uoks0XUl45St3FlatVqef9prk4jRDzhW6WZg+3bk93y6pLjA==
+
+strnum@^1.0.5:
+  version "1.0.5"
+  resolved "https://registry.yarnpkg.com/strnum/-/strnum-1.0.5.tgz#5c4e829fe15ad4ff0d20c3db5ac97b73c9b072db"
+  integrity sha512-J8bbNyKKXl5qYcR36TIO8W3mVGVHrmmxsd5PAItGkmyzwJvybiw2IVq5nqd0i4LSNSkB/sx9VHllbfFdr9k1JA==
+
+supports-preserve-symlinks-flag@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/supports-preserve-symlinks-flag/-/supports-preserve-symlinks-flag-1.0.0.tgz#6eda4bd344a3c94aea376d4cc31bc77311039e09"
+  integrity sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==
+
+tar@^6.1.11, tar@^6.1.2:
+  version "6.2.0"
+  resolved "https://registry.yarnpkg.com/tar/-/tar-6.2.0.tgz#b14ce49a79cb1cd23bc9b016302dea5474493f73"
+  integrity sha512-/Wo7DcT0u5HUV486xg675HtjNd3BXZ6xDbzsCUZPt5iw8bTQ63bP0Raut3mvro9u+CUyq7YQd8Cx55fsZXxqLQ==
+  dependencies:
+    chownr "^2.0.0"
+    fs-minipass "^2.0.0"
+    minipass "^5.0.0"
+    minizlib "^2.1.1"
+    mkdirp "^1.0.3"
+    yallist "^4.0.0"
+
+tmp@^0.2.1:
+  version "0.2.3"
+  resolved "https://registry.yarnpkg.com/tmp/-/tmp-0.2.3.tgz#eb783cc22bc1e8bebd0671476d46ea4eb32a79ae"
+  integrity sha512-nZD7m9iCPC5g0pYmcaxogYKggSfLsdxl8of3Q/oIbqCqLLIO9IAF0GWjX1z9NZRHPiXv8Wex4yDCaZsgEw0Y8w==
+
+treeverse@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/treeverse/-/treeverse-3.0.0.tgz#dd82de9eb602115c6ebd77a574aae67003cb48c8"
+  integrity sha512-gcANaAnd2QDZFmHFEOF4k7uc1J/6a6z3DJMd/QwEyxLoKGiptJRwid582r7QIsFlFMIZ3SnxfS52S4hm2DHkuQ==
+
+ts-node@^7.0.1:
+  version "7.0.1"
+  resolved "https://registry.yarnpkg.com/ts-node/-/ts-node-7.0.1.tgz#9562dc2d1e6d248d24bc55f773e3f614337d9baf"
+  integrity sha512-BVwVbPJRspzNh2yfslyT1PSbl5uIk03EZlb493RKHN4qej/D06n1cEhjlOJG69oFsE7OT8XjpTUcYf6pKTLMhw==
+  dependencies:
+    arrify "^1.0.0"
+    buffer-from "^1.1.0"
+    diff "^3.1.0"
+    make-error "^1.1.1"
+    minimist "^1.2.0"
+    mkdirp "^0.5.1"
+    source-map-support "^0.5.6"
+    yn "^2.0.0"
+
+tslib@^1.11.1:
+  version "1.14.1"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-1.14.1.tgz#cf2d38bdc34a134bcaf1091c41f6619e2f672d00"
+  integrity sha512-Xni35NKzjgMrwevysHTCArtLDpPvye8zV/0E4EyYn43P7/7qvQwPh9BGkHewbMulVntbigmcT7rdX3BNo9wRJg==
+
+tslib@^2.3.1, tslib@^2.6.2:
+  version "2.6.2"
+  resolved "https://registry.yarnpkg.com/tslib/-/tslib-2.6.2.tgz#703ac29425e7b37cd6fd456e92404d46d1f3e4ae"
+  integrity sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==
+
+tuf-js@^2.2.0:
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/tuf-js/-/tuf-js-2.2.0.tgz#4daaa8620ba7545501d04dfa933c98abbcc959b9"
+  integrity sha512-ZSDngmP1z6zw+FIkIBjvOp/II/mIub/O7Pp12j1WNsiCpg5R5wAc//i555bBQsE44O94btLt0xM/Zr2LQjwdCg==
+  dependencies:
+    "@tufjs/models" "2.0.0"
+    debug "^4.3.4"
+    make-fetch-happen "^13.0.0"
+
+typescript@~3.8.3:
+  version "3.8.3"
+  resolved "https://registry.yarnpkg.com/typescript/-/typescript-3.8.3.tgz#409eb8544ea0335711205869ec458ab109ee1061"
+  integrity sha512-MYlEfn5VrLNsgudQTVJeNaQFUAI7DkhnOjdpAp4T+ku1TfQClewlbSuTVHiA+8skNBgaf02TL/kLOvig4y3G8w==
+
+undici-types@~5.26.4:
+  version "5.26.5"
+  resolved "https://registry.yarnpkg.com/undici-types/-/undici-types-5.26.5.tgz#bcd539893d00b56e964fd2657a4866b221a65617"
+  integrity sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==
+
+unique-filename@^3.0.0:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/unique-filename/-/unique-filename-3.0.0.tgz#48ba7a5a16849f5080d26c760c86cf5cf05770ea"
+  integrity sha512-afXhuC55wkAmZ0P18QsVE6kp8JaxrEokN2HGIoIVv2ijHQd419H0+6EigAFcIzXeMIkcIkNBpB3L/DXB3cTS/g==
+  dependencies:
+    unique-slug "^4.0.0"
+
+unique-slug@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/unique-slug/-/unique-slug-4.0.0.tgz#6bae6bb16be91351badd24cdce741f892a6532e3"
+  integrity sha512-WrcA6AyEfqDX5bWige/4NQfPZMtASNVxdmWR76WESYQVAACSgWcR6e9i0mofqqBxYFtL4oAxPIptY73/0YE1DQ==
+  dependencies:
+    imurmurhash "^0.1.4"
+
+upath@^1.1.0:
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/upath/-/upath-1.2.0.tgz#8f66dbcd55a883acdae4408af8b035a5044c1894"
+  integrity sha512-aZwGpamFO61g3OlfT7OQCHqhGnW43ieH9WZeP7QxN/G/jS4jfqUkZxoryvJgVPEcrl5NL/ggHsSmLMHuH64Lhg==
+
+url@0.10.3:
+  version "0.10.3"
+  resolved "https://registry.yarnpkg.com/url/-/url-0.10.3.tgz#021e4d9c7705f21bbf37d03ceb58767402774c64"
+  integrity sha512-hzSUW2q06EqL1gKM/a+obYHLIO6ct2hwPuviqTTOcfFVc61UbfJ2Q32+uGL/HCPxKqrdGB5QUwIe7UqlDgwsOQ==
+  dependencies:
+    punycode "1.3.2"
+    querystring "0.2.0"
+
+util-deprecate@^1.0.2:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
+  integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==
+
+util@^0.12.4:
+  version "0.12.5"
+  resolved "https://registry.yarnpkg.com/util/-/util-0.12.5.tgz#5f17a6059b73db61a875668781a1c2b136bd6fbc"
+  integrity sha512-kZf/K6hEIrWHI6XqOFUiiMa+79wE/D8Q+NCNAWclkyg3b4d2k7s0QGepNjiABc+aR3N1PAyHL7p6UcLY6LmrnA==
+  dependencies:
+    inherits "^2.0.3"
+    is-arguments "^1.0.4"
+    is-generator-function "^1.0.7"
+    is-typed-array "^1.1.3"
+    which-typed-array "^1.1.2"
+
+uuid@8.0.0:
+  version "8.0.0"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-8.0.0.tgz#bc6ccf91b5ff0ac07bbcdbf1c7c4e150db4dbb6c"
+  integrity sha512-jOXGuXZAWdsTH7eZLtyXMqUb9EcWMGZNbL9YcGBJl4MH4nrxHmZJhEHvyLFrkxo+28uLb/NYRcStH48fnD0Vzw==
+
+uuid@^8.3.2:
+  version "8.3.2"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-8.3.2.tgz#80d5b5ced271bb9af6c445f21a1a04c606cefbe2"
+  integrity sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==
+
+uuid@^9.0.1:
+  version "9.0.1"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-9.0.1.tgz#e188d4c8853cc722220392c424cd637f32293f30"
+  integrity sha512-b+1eJOlsR9K8HJpow9Ok3fiWOWSIcIzXodvv0rQjVoOVNpWMpxf1wZNpt4y9h10odCNrqnYp1OBzRktckBe3sA==
+
+validate-npm-package-license@^3.0.4:
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/validate-npm-package-license/-/validate-npm-package-license-3.0.4.tgz#fc91f6b9c7ba15c857f4cb2c5defeec39d4f410a"
+  integrity sha512-DpKm2Ui/xN7/HQKCtpZxoRWBhZ9Z0kqtygG8XCgNQ8ZlDnxuQmWhj566j8fN4Cu3/JmbhsDo7fcAJq4s9h27Ew==
+  dependencies:
+    spdx-correct "^3.0.0"
+    spdx-expression-parse "^3.0.0"
+
+validate-npm-package-name@^5.0.0:
+  version "5.0.0"
+  resolved "https://registry.yarnpkg.com/validate-npm-package-name/-/validate-npm-package-name-5.0.0.tgz#f16afd48318e6f90a1ec101377fa0384cfc8c713"
+  integrity sha512-YuKoXDAhBYxY7SfOKxHBDoSyENFeW5VvIIQp2TGQuit8gpK6MnWaQelBKxso72DoxTZfZdcP3W90LqpSkgPzLQ==
+  dependencies:
+    builtins "^5.0.0"
+
+walk-up-path@^3.0.1:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/walk-up-path/-/walk-up-path-3.0.1.tgz#c8d78d5375b4966c717eb17ada73dbd41490e886"
+  integrity sha512-9YlCL/ynK3CTlrSRrDxZvUauLzAswPCrsaCgilqFevUYpeEW0/3ScEjaa3kbW/T0ghhkEr7mv+fpjqn1Y1YuTA==
+
+which-typed-array@^1.1.14, which-typed-array@^1.1.2:
+  version "1.1.15"
+  resolved "https://registry.yarnpkg.com/which-typed-array/-/which-typed-array-1.1.15.tgz#264859e9b11a649b388bfaaf4f767df1f779b38d"
+  integrity sha512-oV0jmFtUky6CXfkqehVvBP/LSWJ2sy4vWMioiENyJLePrBO/yKyV9OyJySfAKosh+RYkIl5zJCNZ8/4JncrpdA==
+  dependencies:
+    available-typed-arrays "^1.0.7"
+    call-bind "^1.0.7"
+    for-each "^0.3.3"
+    gopd "^1.0.1"
+    has-tostringtag "^1.0.2"
+
+which@^2.0.1:
+  version "2.0.2"
+  resolved "https://registry.yarnpkg.com/which/-/which-2.0.2.tgz#7c6a8dd0a636a0327e10b59c9286eee93f3f51b1"
+  integrity sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==
+  dependencies:
+    isexe "^2.0.0"
+
+which@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/which/-/which-4.0.0.tgz#cd60b5e74503a3fbcfbf6cd6b4138a8bae644c1a"
+  integrity sha512-GlaYyEb07DPxYCKhKzplCWBJtvxZcZMrL+4UkrTSJHHPyZU4mYYTv3qaOe77H7EODLSSopAUFAc6W8U4yqvscg==
+  dependencies:
+    isexe "^3.1.1"
+
+wide-align@^1.1.5:
+  version "1.1.5"
+  resolved "https://registry.yarnpkg.com/wide-align/-/wide-align-1.1.5.tgz#df1d4c206854369ecf3c9a4898f1b23fbd9d15d3"
+  integrity sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==
+  dependencies:
+    string-width "^1.0.2 || 2 || 3 || 4"
+
+"wrap-ansi-cjs@npm:wrap-ansi@^7.0.0", wrap-ansi@^7.0.0:
+  version "7.0.0"
+  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-7.0.0.tgz#67e145cff510a6a6984bdf1152911d69d2eb9e43"
+  integrity sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==
+  dependencies:
+    ansi-styles "^4.0.0"
+    string-width "^4.1.0"
+    strip-ansi "^6.0.0"
+
+wrap-ansi@^8.1.0:
+  version "8.1.0"
+  resolved "https://registry.yarnpkg.com/wrap-ansi/-/wrap-ansi-8.1.0.tgz#56dc22368ee570face1b49819975d9b9a5ead214"
+  integrity sha512-si7QWI6zUMq56bESFvagtmzMdGOtoxfR+Sez11Mobfc7tm+VkUckk9bW2UeffTGVUbOksxmSw0AA2gs8g71NCQ==
+  dependencies:
+    ansi-styles "^6.1.0"
+    string-width "^5.0.1"
+    strip-ansi "^7.0.1"
+
+wrappy@1:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/wrappy/-/wrappy-1.0.2.tgz#b5243d8f3ec1aa35f1364605bc0d1036e30ab69f"
+  integrity sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==
+
+write-file-atomic@^5.0.0:
+  version "5.0.1"
+  resolved "https://registry.yarnpkg.com/write-file-atomic/-/write-file-atomic-5.0.1.tgz#68df4717c55c6fa4281a7860b4c2ba0a6d2b11e7"
+  integrity sha512-+QU2zd6OTD8XWIJCbffaiQeH9U73qIqafo1x6V1snCWYGJf6cVE0cDR4D8xRzcEnfI21IFrUPzPGtcPf8AC+Rw==
+  dependencies:
+    imurmurhash "^0.1.4"
+    signal-exit "^4.0.1"
+
+xml2js@0.6.2:
+  version "0.6.2"
+  resolved "https://registry.yarnpkg.com/xml2js/-/xml2js-0.6.2.tgz#dd0b630083aa09c161e25a4d0901e2b2a929b499"
+  integrity sha512-T4rieHaC1EXcES0Kxxj4JWgaUQHDk+qwHcYOCFHfiwKz7tOVPLq7Hjq9dM1WCMhylqMEfP7hMcOIChvotiZegA==
+  dependencies:
+    sax ">=0.6.0"
+    xmlbuilder "~11.0.0"
+
+xmlbuilder@~11.0.0:
+  version "11.0.1"
+  resolved "https://registry.yarnpkg.com/xmlbuilder/-/xmlbuilder-11.0.1.tgz#be9bae1c8a046e76b31127726347d0ad7002beb3"
+  integrity sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA==
+
+y18n@^5.0.5:
+  version "5.0.8"
+  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.8.tgz#7f4934d0f7ca8c56f95314939ddcd2dd91ce1d55"
+  integrity sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==
+
+yallist@^4.0.0:
+  version "4.0.0"
+  resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
+  integrity sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==
+
+yargs-parser@^21.1.1:
+  version "21.1.1"
+  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-21.1.1.tgz#9096bceebf990d21bb31fa9516e0ede294a77d35"
+  integrity sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==
+
+yargs@^17.7.2:
+  version "17.7.2"
+  resolved "https://registry.yarnpkg.com/yargs/-/yargs-17.7.2.tgz#991df39aca675a192b816e1e0363f9d75d2aa269"
+  integrity sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==
+  dependencies:
+    cliui "^8.0.1"
+    escalade "^3.1.1"
+    get-caller-file "^2.0.5"
+    require-directory "^2.1.1"
+    string-width "^4.2.3"
+    y18n "^5.0.5"
+    yargs-parser "^21.1.1"
+
+yn@^2.0.0:
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/yn/-/yn-2.0.0.tgz#e5adabc8acf408f6385fc76495684c88e6af689a"
+  integrity sha512-uTv8J/wiWTgUTg+9vLTi//leUl5vDQS6uii/emeTb2ssY7vl6QWf2fFbIIGjnhjvbdKlU0ed7QPgY1htTC86jQ==
+
+yocto-queue@^1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/yocto-queue/-/yocto-queue-1.0.0.tgz#7f816433fb2cbc511ec8bf7d263c3b58a1a3c251"
+  integrity sha512-9bnSc/HEW2uRy67wc+T8UwauLuPJVn28jb+GtJY16iiKWyvmYJRXVT4UamsAEGQfPohgr2q4Tq0sQbQlxTfi1g==
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index db433b4..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,7331 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
-
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-version = "23.2.1"
-description = "File support for asyncio."
-optional = false
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-]
-
-[[package]]
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-version = "2.4.3"
-description = "Happy Eyeballs for asyncio"
-optional = false
-python-versions = ">=3.8"
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.0"
-
-[[package]]
-name = "zipp"
-version = "3.20.2"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
-    {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
-]
-
-[package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
-cover = ["pytest-cov"]
-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
-test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = "~3.11"
-content-hash = "062b5dc75dfe8aee57d2e8b894bd22a9b1d4d4ddd328b9ec6134e8881f34c737"
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 9190d37..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,62 +0,0 @@
-[tool.poetry]
-name = "llm-twin-course"
-description = ""
-version = "0.1.0"
-authors = [
-    "Paul Iusztin <p.b.iusztin@gmail.com>",
-    "Vesa Alexandru <vesaalexandru95@gmail.com>",
-    "Razvant Alexandru <AlexandruRazvant@gmail.com>",
-    "Rares Istoc <istoc.rares@gmail.com>",
-    "Vlad Adumitracesei <vladadumitracesei02@gmail.com>",
-    "Anca Muscalagiu <ancamuscalagiu1@gmail.com>"
-]
-package-mode = false
-readme = "README.md"
-
-[tool.ruff]
-line-length = 88
-select = [
-    "F401",
-    "F403",
-]
-
-[tool.poetry.dependencies]
-python = "~3.11"
-pydantic = "^2.6.3"
-pydantic-settings = "^2.2.0"
-pika = "^1.3.2"
-qdrant-client = "^1.8.0"
-aws-lambda-powertools = "^2.38.1"
-selenium = "4.21.0"
-instructorembedding = "^1.0.1"
-numpy = "^1.26.4"
-gdown = "^5.1.0"
-pymongo = "^4.7.1"
-structlog = "^24.1.0"
-rich = "^13.7.1"
-comet-ml = "^3.41.0"
-opik = "1.0.1"
-ruff = "^0.4.3"
-pandas = "^2.0.3"
-datasets = "^2.19.1"
-scikit-learn = "^1.4.2" 
-unstructured = "^0.14.2"
-litellm = "^1.50.4"
-langchain = "^0.2.11"
-langchain-openai = "^0.1.3"
-langchain-community = "^0.2.11"
-html2text = "^2024.2.26"
-huggingface-hub = "0.25.1"
-sagemaker = ">=2.232.2"
-sentence-transformers = "^2.2.2"
-gradio = "^5.5.0"
-
-[tool.poetry.group.feature_pipeline.dependencies]
-bytewax = "0.18.2"
-
-[tool.poetry.group.superlinked_rag.dependencies]
-superlinked = "^7.2.1"
-
-[build-system]
-requires = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
diff --git a/src/bonus_superlinked_rag/README.md b/src/bonus_superlinked_rag/README.md
deleted file mode 100644
index 8ea1b03..0000000
--- a/src/bonus_superlinked_rag/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# Dependencies
-
-- [Docker](https://www.docker.com/)
-- [Poetry](https://python-poetry.org/)
-- [PyEnv](https://github.com/pyenv/pyenv)
-- [Python](https://www.python.org/)
-- [GNU Make](https://www.gnu.org/software/make/)
-
-# Install
-
-## 1. Start the Superlinked server
-
-Make sure you have `pyenv` installed. For example, on MacOS you can install it as:
-```shell
-brew update
-brew install pyenv
-```
-
-Now, let's start the Superlinked server by running the following commands:
-```shell
-# Create a virtual environment and install all necessary dependencies to deploy the server.
-cd 6-bonus-superlinked-rag/server
-./tools/init-venv.sh
-cd runner
-source "$(poetry env info --path)/bin/activate"
-cd ..
-
-# Make sure you have your docker engine running (e.g. start the docker desktop app).
-./tools/deploy.py up
-```
-
-> [!NOTE]
-> After the server started, you can check out it works and also it's API at http://localhost:8080/docs/
-
-You can test that the Superlinked server started successfully by running the following command from the `root directory` of the `llm-twin-course`:
-```
-make test-superlinked-server
-```
-You should see that some mock data has been sent to the Superlinked server and it was queried successfully. 
-
-## 2. Start the rest of the infrastructure
-
-After your Superlinked server successfully started (from the root of the repository), run the following to start all necessary components to run locally the LLM twin project powered by Superlinked:
-```shell
-make local-start-superlinked
-```
-
-> [!IMPORTANT]
-> Before starting, ensure you have your `.env` file filled with everything required to run the system. 
->
-> For more details on setting up the local infrastructure, you can check out the course's main [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) document.
-
-To stop the local infrastructure, run:
-```shell
-make local-stop-superlinked
-```
-
-> [!NOTE]
-> After running the ingestion pipeline, you can visualize what's inside the Redis vector DB at http://localhost:8001/redis-stack/browser
-
-
-# Usage
-
-To trigger the ingestion, run:
-```shell
-make local-test-github
-# #OR
-make local-test-medium
-```
-You can use other GitHub or Medium links to populate the vector DB with more data.
-
-To call the retrieval module and query the Superlinked server & vector DB, run:
-```shell
-make local-test-retriever-superlinked
-```
-
-> [!IMPORTANT]
-> You can check out the main [INSTALL_AND_USAGE](https://github.com/decodingml/llm-twin-course/blob/main/INSTALL_AND_USAGE.md) document of the course for more details on an end-to-end flow.
-
-
-# Next steps
-
-If you enjoyed our [Superlinked](https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse) bonus series, we recommend checking out their site for more examples. As Superlinked is not just a RAG tool but a general vector compute engine, you can build other awesome stuff with it, such as recommender systems. 
-
-→ 🔗 More on [Superlinked](https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse) ←
diff --git a/src/bonus_superlinked_rag/config.py b/src/bonus_superlinked_rag/config.py
deleted file mode 100644
index 9348e00..0000000
--- a/src/bonus_superlinked_rag/config.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from pydantic_settings import BaseSettings
-
-
-class Settings(BaseSettings):
-    # Embeddings config
-    EMBEDDING_MODEL_ID: str = "BAAI/bge-small-en-v1.5"
-
-    # MQ config
-    RABBITMQ_DEFAULT_USERNAME: str = "guest"
-    RABBITMQ_DEFAULT_PASSWORD: str = "guest"
-    RABBITMQ_HOST: str = "mq"  # or localhost if running outside Docker
-    RABBITMQ_PORT: int = 5672
-    RABBITMQ_QUEUE_NAME: str = "default"
-
-    # Superlinked
-    SUPERLINKED_SERVER_URL: str = (
-        "http://executor:8080"  # # or http://localhost:8080 if running outside Docker
-    )
-
-    # OpenAI
-    OPENAI_MODEL_ID: str = "gpt-4o-mini"
-    OPENAI_API_KEY: str | None = None
-
-
-settings = Settings()
diff --git a/src/bonus_superlinked_rag/data_flow/stream_input.py b/src/bonus_superlinked_rag/data_flow/stream_input.py
deleted file mode 100644
index 0081f86..0000000
--- a/src/bonus_superlinked_rag/data_flow/stream_input.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import json
-import time
-from datetime import datetime
-from typing import Generic, Iterable, List, Optional, TypeVar
-
-from bytewax.inputs import FixedPartitionedSource, StatefulSourcePartition
-from config import settings
-from mq import RabbitMQConnection
-from utils.logging import get_logger
-
-logger = get_logger(__name__)
-
-DataT = TypeVar("DataT")
-MessageT = TypeVar("MessageT")
-
-
-class RabbitMQSource(FixedPartitionedSource, Generic[DataT, MessageT]):
-    def list_parts(self) -> List[str]:
-        return ["single partition"]
-
-    def build_part(
-        self, now: datetime, for_part: str, resume_state: MessageT | None = None
-    ) -> StatefulSourcePartition[DataT, MessageT]:
-        return RabbitMQPartition(queue_name=settings.RABBITMQ_QUEUE_NAME)
-
-
-class RabbitMQPartition(StatefulSourcePartition, Generic[DataT, MessageT]):
-    """
-    Class responsible for creating a connection between bytewax and rabbitmq that facilitates the transfer of data from mq to bytewax streaming piepline.
-    Inherits StatefulSourcePartition for snapshot functionality that enables saving the state of the queue
-    """
-
-    def __init__(self, queue_name: str, resume_state: MessageT | None = None) -> None:
-        self._in_flight_msg_ids = resume_state or set()
-        self.queue_name = queue_name
-        self.connection = RabbitMQConnection()
-
-        try:
-            self.connection.connect()
-            self.channel = self.connection.get_channel()
-        except Exception:
-            logger.warning(
-                f"Error while trying to connect to the queue and get the current channel {self.queue_name}",
-            )
-
-    def next_batch(self, sched: Optional[datetime]) -> Iterable[DataT]:
-        try:
-            method_frame, header_frame, body = self.channel.basic_get(
-                queue=self.queue_name, auto_ack=True
-            )
-        except Exception:
-            logger.warning(
-                f"Error while fetching message from queue: {self.queue_name}", 
-            )
-            time.sleep(10)  # Sleep for 10 seconds before retrying to access the queue.
-
-            self.connection.connect()
-            self.channel = self.connection.get_channel()
-
-            return []
-
-        if method_frame:
-            message_id = method_frame.delivery_tag
-            self._in_flight_msg_ids.add(message_id)
-
-            return [json.loads(body)]
-        else:
-            return []
-
-    def snapshot(self) -> MessageT:
-        return self._in_flight_msg_ids
-
-    def garbage_collect(self, state):
-        closed_in_flight_msg_ids = state
-        for msg_id in closed_in_flight_msg_ids:
-            self.channel.basic_ack(delivery_tag=msg_id)
-            self._in_flight_msg_ids.remove(msg_id)
-
-    def close(self):
-        self.channel.close()
diff --git a/src/bonus_superlinked_rag/data_flow/stream_output.py b/src/bonus_superlinked_rag/data_flow/stream_output.py
deleted file mode 100644
index c70ee2f..0000000
--- a/src/bonus_superlinked_rag/data_flow/stream_output.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from bytewax.outputs import DynamicSink, StatelessSinkPartition
-from models.documents import Document
-from superlinked_client import SuperlinkedClient
-from tqdm import tqdm
-from utils.logging import get_logger
-
-logger = get_logger(__name__)
-
-
-class SuperlinkedOutputSink(DynamicSink):
-    def __init__(self, client: SuperlinkedClient) -> None:
-        self._client = client
-
-    def build(self, worker_index: int, worker_count: int) -> StatelessSinkPartition:
-        return SuperlinkedSinkPartition(client=self._client)
-
-
-class SuperlinkedSinkPartition(StatelessSinkPartition):
-    def __init__(self, client: SuperlinkedClient):
-        self._client = client
-
-    def write_batch(self, items: list[Document]) -> None:
-        for item in tqdm(items, desc="Sending items to Superlinked..."):
-            match item.type:
-                case "repositories":
-                    self._client.ingest_repository(item)
-                case "posts":
-                    self._client.ingest_post(item)
-                case "articles":
-                    self._client.ingest_article(item)
-                case _:
-                    logger.error(f"Unknown item type: {item.type}")
diff --git a/src/bonus_superlinked_rag/data_logic/cleaning_data_handlers.py b/src/bonus_superlinked_rag/data_logic/cleaning_data_handlers.py
deleted file mode 100644
index 0d2d12a..0000000
--- a/src/bonus_superlinked_rag/data_logic/cleaning_data_handlers.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from abc import ABC, abstractmethod
-
-from models.documents import ArticleDocument, Document, PostDocument, RepositoryDocument
-from models.raw import ArticleRawModel, PostsRawModel, RawModel, RepositoryRawModel
-from utils.cleaning import clean_text
-
-from .splitters import split_text
-
-
-class CleaningDataHandler(ABC):
-    """
-    Abstract class for all cleaning data handlers.
-    All data transformations logic for the cleaning step is done here
-    """
-
-    @abstractmethod
-    def clean(self, data_model: RawModel) -> list[Document]:
-        pass
-
-
-class PostCleaningHandler(CleaningDataHandler):
-    def clean(self, data_model: PostsRawModel) -> list[PostDocument]:
-        documents = []
-        cleaned_text = clean_text("".join(data_model.content.values()))
-        for post_subsection in split_text(cleaned_text):
-            documents.append(
-                PostDocument(
-                    id=data_model.id,
-                    platform=data_model.platform,
-                    content=post_subsection,
-                    author_id=data_model.author_id,
-                    type=data_model.type,
-                )
-            )
-
-        return documents
-
-
-class ArticleCleaningHandler(CleaningDataHandler):
-    def clean(self, data_model: ArticleRawModel) -> list[ArticleDocument]:
-        documents = []
-        cleaned_text = clean_text("".join(data_model.content.values()))
-        for article_subsection in split_text(cleaned_text):
-            documents.append(
-                ArticleDocument(
-                    id=data_model.id,
-                    platform=data_model.platform,
-                    link=data_model.link,
-                    content=article_subsection,
-                    author_id=data_model.author_id,
-                    type=data_model.type,
-                )
-            )
-
-        return documents
-
-
-class RepositoryCleaningHandler(CleaningDataHandler):
-    def clean(self, data_model: RepositoryRawModel) -> list[RepositoryDocument]:
-        documents = []
-        for file_name, file_content in data_model.content.items():
-            cleaned_file_content = clean_text(file_content)
-            for file_subsection in split_text(cleaned_file_content):
-                documents.append(
-                    RepositoryDocument(
-                        id=data_model.id,
-                        platform=data_model.platform,
-                        name=f"{data_model.name}:{file_name}",
-                        link=data_model.link,
-                        content=file_subsection,
-                        author_id=data_model.owner_id,
-                        type=data_model.type,
-                    )
-                )
-
-        return documents
diff --git a/src/bonus_superlinked_rag/data_logic/dispatchers.py b/src/bonus_superlinked_rag/data_logic/dispatchers.py
deleted file mode 100644
index c64a2da..0000000
--- a/src/bonus_superlinked_rag/data_logic/dispatchers.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from data_logic.cleaning_data_handlers import (
-    ArticleCleaningHandler,
-    CleaningDataHandler,
-    PostCleaningHandler,
-    RepositoryCleaningHandler,
-)
-from models.documents import Document
-from models.raw import ArticleRawModel, PostsRawModel, RawModel, RepositoryRawModel
-from utils.logging import get_logger
-
-logger = get_logger(__name__)
-
-
-class RawDispatcher:
-    @staticmethod
-    def handle_mq_message(message: dict) -> RawModel:
-        data_type = message.get("type")
-
-        logger.info("Received raw message.", data_type=data_type)
-
-        if data_type == "posts":
-            return PostsRawModel(**message)
-        elif data_type == "articles":
-            return ArticleRawModel(**message)
-        elif data_type == "repositories":
-            return RepositoryRawModel(**message)
-        else:
-            raise ValueError(f"Unsupported data type: {data_type}")
-
-
-class CleaningHandlerFactory:
-    @staticmethod
-    def create_handler(data_type: str) -> CleaningDataHandler:
-        if data_type == "posts":
-            return PostCleaningHandler()
-        elif data_type == "articles":
-            return ArticleCleaningHandler()
-        elif data_type == "repositories":
-            return RepositoryCleaningHandler()
-        else:
-            raise ValueError("Unsupported data type")
-
-
-class CleaningDispatcher:
-    cleaning_factory = CleaningHandlerFactory()
-
-    @classmethod
-    def dispatch_cleaner(cls, data_model: RawModel) -> list[Document]:
-        logger.info("Cleaning data.", data_type=data_model.type)
-
-        data_type = data_model.type
-        handler = cls.cleaning_factory.create_handler(data_type)
-        cleaned_models = handler.clean(data_model)
-
-        logger.info(
-            "Data cleaned successfully.",
-            data_type=data_type,
-            len_cleaned_documents=len(cleaned_models),
-            len_content=sum([len(doc.content) for doc in cleaned_models]),
-        )
-
-        return cleaned_models
diff --git a/src/bonus_superlinked_rag/data_logic/splitters.py b/src/bonus_superlinked_rag/data_logic/splitters.py
deleted file mode 100644
index 41e82ed..0000000
--- a/src/bonus_superlinked_rag/data_logic/splitters.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-
-
-def split_text(text: str) -> list[str]:
-    character_splitter = RecursiveCharacterTextSplitter(
-        separators=["\n\n"], chunk_size=2000, chunk_overlap=0
-    )
-    chunks = character_splitter.split_text(text)
-
-    return chunks
diff --git a/src/bonus_superlinked_rag/llm/prompt_templates.py b/src/bonus_superlinked_rag/llm/prompt_templates.py
deleted file mode 100644
index efd8a60..0000000
--- a/src/bonus_superlinked_rag/llm/prompt_templates.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from abc import ABC, abstractmethod
-
-from langchain.prompts import PromptTemplate
-from pydantic import BaseModel
-
-
-class BasePromptTemplate(ABC, BaseModel):
-    @abstractmethod
-    def create_template(self) -> PromptTemplate:
-        pass
-
-
-class QueryExpansionTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to generate {to_expand_to_n}
-    different versions of the given user question to retrieve relevant documents from a vector
-    database. By generating multiple perspectives on the user question, your goal is to help
-    the user overcome some of the limitations of the distance-based similarity search.
-    Provide these alternative questions separated by '{separator}'.
-    Original question: {question}"""
-
-    @property
-    def separator(self) -> str:
-        return "#next-question#"
-
-    def create_template(self, to_expand_to_n: int) -> PromptTemplate:
-        return PromptTemplate(
-            template=self.prompt,
-            input_variables=["question"],
-            partial_variables={
-                "separator": self.separator,
-                "to_expand_to_n": to_expand_to_n,
-            },
-        )
-
-
-class SelfQueryTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to extract information from a user question.
-    The required information that needs to be extracted is the user or author id. 
-    Your response should consists of only the extracted id (e.g. 1345256), nothing else.
-    If you cannot find the author id, return the string "None".
-    User question: {question}"""
-
-    def create_template(self) -> PromptTemplate:
-        return PromptTemplate(template=self.prompt, input_variables=["question"])
-
-
-class RerankingTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to rerank passages related to a query
-    based on their relevance. 
-    The most relevant passages should be put at the beginning. If no passes are relevant, keep the original order. Even if you find duplicates, return them all.
-    You should only pick at max {keep_top_k} passages. When no passages are relevant, pick at max top {keep_top_k} passages.
-    The provided and reranked documents are separated by '{separator}'.
-    
-    The following are passages related to this query: {question}.
-    
-    Passages: 
-    {passages}
-    """
-
-    def create_template(self, keep_top_k: int) -> PromptTemplate:
-        return PromptTemplate(
-            template=self.prompt,
-            input_variables=["question", "passages"],
-            partial_variables={"keep_top_k": keep_top_k, "separator": self.separator},
-        )
-
-    @property
-    def separator(self) -> str:
-        return "\n#next-document#\n"
diff --git a/src/bonus_superlinked_rag/local_test.py b/src/bonus_superlinked_rag/local_test.py
deleted file mode 100644
index dba549f..0000000
--- a/src/bonus_superlinked_rag/local_test.py
+++ /dev/null
@@ -1,162 +0,0 @@
-import pprint
-
-from models.documents import ArticleDocument, PostDocument, RepositoryDocument
-from superlinked_client import SuperlinkedClient
-
-mock_data_articles = [
-    ArticleDocument(
-        id="1",
-        platform="Twitter",
-        link="http://twitter.com/1",
-        content="Cleaned content 1",
-        author_id="author_1",
-        type="social",
-    ),
-    ArticleDocument(
-        id="2",
-        platform="Facebook",
-        link="http://facebook.com/2",
-        content="Cleaned content 2",
-        author_id="author_2",
-        type="social",
-    ),
-    ArticleDocument(
-        id="3",
-        platform="LinkedIn",
-        link="http://linkedin.com/3",
-        content="Cleaned content 3",
-        author_id="author_3",
-        type="professional",
-    ),
-    ArticleDocument(
-        id="4",
-        platform="Medium",
-        link="http://medium.com/4",
-        content="Cleaned content 4",
-        author_id="author_4",
-        type="blog",
-    ),
-    ArticleDocument(
-        id="5",
-        platform="Reddit",
-        link="http://reddit.com/5",
-        content="Cleaned content 5",
-        author_id="author_5",
-        type="forum",
-    ),
-]
-
-mock_data_posts = [
-    PostDocument(
-        id="1",
-        platform="Twitter",
-        content="Cleaned content 1",
-        author_id="author_1",
-        type="social",
-    ),
-    PostDocument(
-        id="2",
-        platform="Facebook",
-        content="Cleaned content 2",
-        author_id="author_2",
-        type="social",
-    ),
-    PostDocument(
-        id="3",
-        platform="LinkedIn",
-        content="Cleaned content 3",
-        author_id="author_3",
-        type="professional",
-    ),
-    PostDocument(
-        id="4",
-        platform="Medium",
-        content="Cleaned content 4",
-        author_id="author_4",
-        type="blog",
-    ),
-    PostDocument(
-        id="5",
-        platform="Reddit",
-        content="Cleaned content 5",
-        author_id="author_5",
-        type="forum",
-    ),
-]
-
-mock_data_repositories = [
-    RepositoryDocument(
-        id="1",
-        platform="GitHub",
-        name="Repo1",
-        link="http://github.com/repo1",
-        content="Cleaned content repo 1",
-        author_id="owner_1",
-        type="public",
-    ),
-    RepositoryDocument(
-        id="2",
-        platform="GitHub",
-        name="Repo2",
-        link="http://gitlab.com/repo2",
-        content="Cleaned content repo 2",
-        author_id="owner_2",
-        type="private",
-    ),
-    RepositoryDocument(
-        id="3",
-        platform="GitHub",
-        name="Repo3",
-        link="http://bitbucket.com/repo3",
-        content="Cleaned content repo 3",
-        author_id="owner_3",
-        type="public",
-    ),
-    RepositoryDocument(
-        id="4",
-        platform="GitHub",
-        name="Repo4",
-        link="http://github.com/repo4",
-        content="Cleaned content repo 4",
-        author_id="owner_4",
-        type="public",
-    ),
-    RepositoryDocument(
-        id="5",
-        platform="GitHub",
-        name="Repo5",
-        link="http://gitlab.com/repo5",
-        content="Cleaned content repo 5",
-        author_id="owner_5",
-        type="private",
-    ),
-]
-
-if __name__ == "__main__":
-    client = SuperlinkedClient("http://localhost:8080")
-
-    # Ingest mock data.
-    for article in mock_data_articles:
-        client.ingest_article(article)
-    for post in mock_data_posts:
-        client.ingest_post(post)
-    for repository in mock_data_repositories:
-        client.ingest_repository(repository)
-
-    # Query mock data.
-    pp = pprint.PrettyPrinter(indent=4)
-    
-    print("-" * 200)
-    print("Article query:")
-    pp.pprint(client.search_article("Cleaned content repo 4", "medium", author_id="author_4"))
-    print("-" * 200)
-
-    print("-" * 200)
-    print("Post query:")
-    pp.pprint(client.search_post("Cleaned content repo 4", "linkedin", author_id="author_4"))
-    print("-" * 200)
-
-    print("-" * 200)
-    print("Repository query:")
-    pp.pprint(client.search_repository("Cleaned content repo 4", "github", author_id="author_4"))
-    print("-" * 200)
diff --git a/src/bonus_superlinked_rag/main.py b/src/bonus_superlinked_rag/main.py
deleted file mode 100644
index 38697b1..0000000
--- a/src/bonus_superlinked_rag/main.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import bytewax.operators as op
-from bytewax.dataflow import Dataflow
-
-from data_flow.stream_input import RabbitMQSource
-from data_flow.stream_output import SuperlinkedOutputSink
-from data_logic.dispatchers import (
-    CleaningDispatcher,
-    RawDispatcher,
-)
-from superlinked_client import SuperlinkedClient
-
-
-flow = Dataflow("Streaming RAG feature pipeline")
-stream = op.input("input", flow, RabbitMQSource())
-stream = op.map("raw", stream, RawDispatcher.handle_mq_message)
-stream = op.map("clean", stream, CleaningDispatcher.dispatch_cleaner)
-stream = op.flatten("flatten_final_output", stream)
-op.output(
-    "superlinked_output",
-    stream,
-    SuperlinkedOutputSink(client=SuperlinkedClient()),
-)
diff --git a/src/bonus_superlinked_rag/models/__init__.py b/src/bonus_superlinked_rag/models/__init__.py
deleted file mode 100644
index ed9973f..0000000
--- a/src/bonus_superlinked_rag/models/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from . import documents, raw, utils
-
-__all__ = ["documents", "raw", "utils"]
diff --git a/src/bonus_superlinked_rag/models/documents.py b/src/bonus_superlinked_rag/models/documents.py
deleted file mode 100644
index 68da5a7..0000000
--- a/src/bonus_superlinked_rag/models/documents.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from typing_extensions import Annotated
-from pydantic import BaseModel, BeforeValidator
-
-
-class PostDocument(BaseModel):
-    id: str
-    platform: Annotated[str, BeforeValidator(str.lower)]
-    content: str
-    author_id: str
-    type: str
-
-
-class ArticleDocument(BaseModel):
-    id: str
-    platform: Annotated[str, BeforeValidator(str.lower)]
-    link: str
-    content: str
-    author_id: str
-    type: str
-
-
-class RepositoryDocument(BaseModel):
-    id: str
-    platform: Annotated[str, BeforeValidator(str.lower)]
-    name: str
-    link: str
-    content: str
-    author_id: str
-    type: str
-
-
-Document = PostDocument | ArticleDocument | RepositoryDocument
diff --git a/src/bonus_superlinked_rag/models/raw.py b/src/bonus_superlinked_rag/models/raw.py
deleted file mode 100644
index 81cbf55..0000000
--- a/src/bonus_superlinked_rag/models/raw.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class RepositoryRawModel(BaseModel):
-    id: str = Field(alias="entry_id")
-    type: str
-    platform: str = "github"
-    name: str
-    link: str
-    content: dict
-    owner_id: str
-
-
-class ArticleRawModel(BaseModel):
-    id: str = Field(alias="entry_id")
-    type: str
-    platform: str
-    link: str
-    content: dict
-    author_id: str
-
-
-class PostsRawModel(BaseModel):
-    id: str = Field(alias="entry_id")
-    type: str
-    platform: str
-    content: dict
-    author_id: str | None = None
-    image: Optional[str] = None
-
-
-RawModel = RepositoryRawModel | ArticleRawModel | PostsRawModel
diff --git a/src/bonus_superlinked_rag/models/utils.py b/src/bonus_superlinked_rag/models/utils.py
deleted file mode 100644
index c79f707..0000000
--- a/src/bonus_superlinked_rag/models/utils.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional
-
-import pandas as pd
-from pydantic import BaseModel
-
-from models.documents import Document
-
-
-def pydantic_models_to_dataframe(
-    models: List[BaseModel], index_column: Optional[str] = "id"
-) -> pd.DataFrame:
-    """
-    Converts a list of Pydantic models to a Pandas DataFrame.
-
-    Args:
-    models (List[BaseModel]): List of Pydantic models.
-
-    Returns:
-    pd.DataFrame: DataFrame containing the data from the Pydantic models.
-    """
-
-    if not models:
-        return pd.DataFrame()
-
-    # Convert each model to a dictionary and create a list of dictionaries
-    data = [model.model_dump() for model in models]
-
-    # Create a DataFrame from the list of dictionaries
-    df = pd.DataFrame(data)
-
-    if index_column in df.columns:
-        df["index"] = df[index_column]
-    else:
-        raise RuntimeError(f"Index column '{index_column}' not found in DataFrame.")
-
-    return df
-
-
-def group_by_type(documents: list[Document]) -> Dict[str, list[Document]]:
-    return _group_by(documents, selector=lambda doc: doc.type)
-
-
-def _group_by(documents: list[Document], selector: Callable) -> Dict[Any, list]:
-    grouped = {}
-    for doc in documents:
-        key = selector(doc)
-
-        if key not in grouped:
-            grouped[key] = []
-        grouped[key].append(doc)
-
-    return grouped
diff --git a/src/bonus_superlinked_rag/rag/query_expanison.py b/src/bonus_superlinked_rag/rag/query_expanison.py
deleted file mode 100644
index af54c7a..0000000
--- a/src/bonus_superlinked_rag/rag/query_expanison.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from langchain_openai import ChatOpenAI
-
-from llm.chain import GeneralChain
-from llm.prompt_templates import QueryExpansionTemplate
-from config import settings
-
-
-class QueryExpansion:
-    @staticmethod
-    def generate_response(query: str, to_expand_to_n: int) -> list[str]:
-        query_expansion_template = QueryExpansionTemplate()
-        prompt_template = query_expansion_template.create_template(to_expand_to_n)
-        model = ChatOpenAI(model=settings.OPENAI_MODEL_ID, temperature=0)
-
-        chain = GeneralChain().get_chain(
-            llm=model, output_key="expanded_queries", template=prompt_template
-        )
-
-        response = chain.invoke({"question": query})
-        result = response["expanded_queries"]
-
-        queries = result.strip().split(query_expansion_template.separator)
-        stripped_queries = [
-            stripped_item for item in queries if (stripped_item := item.strip())
-        ]
-
-        return stripped_queries
diff --git a/src/bonus_superlinked_rag/rag/reranking.py b/src/bonus_superlinked_rag/rag/reranking.py
deleted file mode 100644
index 6cfdf9b..0000000
--- a/src/bonus_superlinked_rag/rag/reranking.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from langchain_openai import ChatOpenAI
-
-from llm.chain import GeneralChain
-from llm.prompt_templates import RerankingTemplate
-from config import settings
-
-
-class Reranker:
-    @staticmethod
-    def generate_response(
-        query: str, passages: list[str], keep_top_k: int
-    ) -> list[str]:
-        reranking_template = RerankingTemplate()
-        prompt_template = reranking_template.create_template(keep_top_k=keep_top_k)
-
-        model = ChatOpenAI(model=settings.OPENAI_MODEL_ID)
-        chain = GeneralChain().get_chain(
-            llm=model, output_key="rerank", template=prompt_template
-        )
-
-        stripped_passages = [
-            stripped_item for item in passages if (stripped_item := item.strip())
-        ]
-        passages = reranking_template.separator.join(stripped_passages)
-        response = chain.invoke({"question": query, "passages": passages})
-
-        result = response["rerank"]
-        reranked_passages = result.strip().split(reranking_template.separator)
-        stripped_passages = [
-            stripped_item
-            for item in reranked_passages
-            if (stripped_item := item.strip())
-        ]
-
-        return stripped_passages
diff --git a/src/bonus_superlinked_rag/rag/retriever.py b/src/bonus_superlinked_rag/rag/retriever.py
deleted file mode 100644
index 7811764..0000000
--- a/src/bonus_superlinked_rag/rag/retriever.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import concurrent.futures
-
-import utils
-from models.documents import Document
-from rag.query_expanison import QueryExpansion
-from rag.reranking import Reranker
-from rag.self_query import SelfQuery
-from superlinked_client import SuperlinkedClient
-from utils.logging import get_logger
-
-logger = get_logger(__name__)
-
-
-class VectorRetriever:
-    """
-    Class for retrieving vectors from a Vector store in a RAG system using query expansion and Multitenancy search.
-    """
-
-    def __init__(self, query: str) -> None:
-        self._client = SuperlinkedClient()
-        self.query = query
-        self._query_expander = QueryExpansion()
-        self._metadata_extractor = SelfQuery()
-        self._reranker = Reranker()
-
-    def _search_single_query(
-        self, generated_query: str, author_id: str, k: int
-    ) -> list[Document]:
-        assert k > 3, "k should be greater than 3"
-
-        posts = self._client.search_post(
-            search_query=generated_query,
-            platform="linkedin",
-            author_id=author_id,
-            limit=k // 3,
-        )
-        logger.info("Successfully retrieved posts.", num_posts=len(posts))
-        logger.info("Total posts content length.", total_content_len=sum([len(post.content) for post in posts]))
-        
-        articles = self._client.search_article(
-            search_query=generated_query,
-            platform="medium",
-            author_id=author_id,
-            limit=k // 3,
-        )
-        logger.info("Successfully retrieved articles.", num_articles=len(articles))
-        logger.info("Total articles content length.", total_content_len=sum([len(article.content) for article in articles]))
-        
-        repositories = self._client.search_repository(
-            search_query=generated_query,
-            platform="github",
-            author_id=author_id,
-            limit=k // 3,
-        )
-        logger.info("Successfully retrieved repositories.", num_repositories=len(repositories))
-        logger.info("Total repositories content length.", total_content_len=sum([len(repository.content) for repository in repositories]))
-
-        return posts + articles + repositories
-
-    def retrieve_top_k(self, k: int, to_expand_to_n_queries: int) -> list:
-        generated_queries = self._query_expander.generate_response(
-            self.query, to_expand_to_n=to_expand_to_n_queries
-        )
-        logger.info(
-            "Successfully generated queries for search.",
-            num_queries=len(generated_queries),
-        )
-
-        author_id = self._metadata_extractor.generate_response(self.query)
-        if author_id:
-            logger.info(
-                "Successfully extracted the author_id from the query.",
-                author_id=author_id,
-            )
-        else:
-            logger.info("Couldn't extract the author_id from the query. Defaulting to empty string.")
-            author_id  = ""
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            search_tasks = [
-                executor.submit(self._search_single_query, query, author_id, k)
-                for query in generated_queries
-            ]
-
-            documents = [
-                task.result() for task in concurrent.futures.as_completed(search_tasks)
-            ]
-            documents = utils.flatten(documents)
-
-        logger.info("All documents retrieved successfully.", num_documents=len(documents))
-
-        return documents
-
-    def rerank(self, documents: list[Document], keep_top_k: int) -> list[str]:
-        content_list = [document.content for document in documents]
-        rerank_documents = self._reranker.generate_response(
-            query=self.query, passages=content_list, keep_top_k=keep_top_k
-        )
-
-        logger.info("Documents reranked successfully.", num_documents=len(rerank_documents))
-
-        return rerank_documents
-
-    def set_query(self, query: str) -> None:
-        self.query = query
diff --git a/src/bonus_superlinked_rag/rag/self_query.py b/src/bonus_superlinked_rag/rag/self_query.py
deleted file mode 100644
index 76adc3f..0000000
--- a/src/bonus_superlinked_rag/rag/self_query.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from langchain_openai import ChatOpenAI
-from llm.chain import GeneralChain
-from llm.prompt_templates import SelfQueryTemplate
-from config import settings
-
-
-class SelfQuery:
-    @staticmethod
-    def generate_response(query: str) -> str | None:
-        prompt = SelfQueryTemplate().create_template()
-        model = ChatOpenAI(model=settings.OPENAI_MODEL_ID, temperature=0)
-
-        chain = GeneralChain().get_chain(
-            llm=model, output_key="metadata_filter_value", template=prompt
-        )
-
-        response = chain.invoke({"question": query})
-        result = response.get("metadata_filter_value", "none")
-        
-        if result.lower() == "none":
-            return None
-
-        return result
diff --git a/src/bonus_superlinked_rag/retriever.py b/src/bonus_superlinked_rag/retriever.py
deleted file mode 100644
index e776a5a..0000000
--- a/src/bonus_superlinked_rag/retriever.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from dotenv import load_dotenv
-from langchain.globals import set_verbose
-from rag.retriever import VectorRetriever
-
-from utils.logging import get_logger
-
-set_verbose(False)
-
-logger = get_logger(__name__)
-
-if __name__ == "__main__":
-    load_dotenv()
-    query = """
-        I am author_4.
-        
-        Could you please draft a LinkedIn post discussing RAG systems?
-        I'm particularly interested in how RAG works and how it is integrated with vector DBs and large language models (LLMs).
-        """
-    retriever = VectorRetriever(query=query)
-    hits = retriever.retrieve_top_k(k=6, to_expand_to_n_queries=3)
-
-    reranked_hits = retriever.rerank(documents=hits, keep_top_k=3)
-    
-    logger.info("Reranked hits:")
-    for rank, hit in enumerate(reranked_hits):
-        logger.info(f"{rank}: {hit[:100]}...")
diff --git a/src/bonus_superlinked_rag/server/.python-version b/src/bonus_superlinked_rag/server/.python-version
deleted file mode 100644
index 2c07333..0000000
--- a/src/bonus_superlinked_rag/server/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.11
diff --git a/src/bonus_superlinked_rag/server/LICENSE b/src/bonus_superlinked_rag/server/LICENSE
deleted file mode 100644
index 261eeb9..0000000
--- a/src/bonus_superlinked_rag/server/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
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diff --git a/src/bonus_superlinked_rag/server/NOTICE b/src/bonus_superlinked_rag/server/NOTICE
deleted file mode 100644
index 053540a..0000000
--- a/src/bonus_superlinked_rag/server/NOTICE
+++ /dev/null
@@ -1,1189 +0,0 @@
-Package: superlinked-3.28.1
-Version: 3.28.1
-License: Apache-2.0
-Author: Superlinked Release
-Notice:
-Package: aws_sam_translator-1.85.0
-Version: 1.85.0
-License: Apache License 2.0
-Author: Amazon Web Services
-Notice:
-serverless-application-model
-Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-
-----------------------------------------------------------------------
-Package: boto3-1.34.13
-Version: 1.34.13
-License: Apache License 2.0
-Author: Amazon Web Services
-Notice:
-boto3
-Copyright 2013-2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-
-----------------------------------------------------------------------
-Package: aws_xray_sdk-2.12.1
-Version: 2.12.1
-License: Apache License 2.0
-Author: Amazon Web Services
-Notice:
-Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-
-----------------------------------------------------------------------
-Package: torch-2.2.1
-Version: 2.2.1
-License: BSD-3
-Author: PyTorch Team
-Notice:
-=======================================================================
-Software under third_party
-=======================================================================
-Software libraries under third_party are provided as github submodule
-links, and their content is not part of the Caffe2 codebase. Their
-licences can be found under the respective software repositories.
-
-=======================================================================
-Earlier BSD License
-=======================================================================
-Early development of Caffe2 in 2015 and early 2016 is licensed under the
-BSD license. The license is attached below:
-
-All contributions by Facebook:
-Copyright (c) 2016 Facebook Inc.
-
-All contributions by Google:
-Copyright (c) 2015 Google Inc.
-All rights reserved.
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-All contributions by Yangqing Jia:
-Copyright (c) 2015 Yangqing Jia
-All rights reserved.
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-Copyright 2019-2020 Kakao Brain
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-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
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-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
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-=======================================================================
-Caffe's BSD License
-=======================================================================
-Some parts of the caffe2 code is derived from the original Caffe code, which is
-created by Yangqing Jia and is now a BSD-licensed open-source project. The Caffe
-license is as follows:
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diff --git a/src/bonus_superlinked_rag/server/README.md b/src/bonus_superlinked_rag/server/README.md
deleted file mode 100644
index 91a7481..0000000
--- a/src/bonus_superlinked_rag/server/README.md
+++ /dev/null
@@ -1,69 +0,0 @@
-# Superlinked Server
-
-This repository contains a server implementation of the [Superlinked](https://github.com/superlinked/superlinked) library. It is designed to be used by end users who want to leverage the power of Superlinked in deployable projects. With a single script, you can deploy a Superlinked-powered app instance that creates REST endpoints and connects to external Vector Databases. This makes it an ideal solution for those seeking an easy-to-deploy environment for their Superlinked projects.
-
-## Prerequisites
-
-Before you can use this environment, you need to ensure that you have the following prerequisites:
-
-- A local setup or a server on AWS or GCP. Please note that Windows is not supported, you should use Linux or MacOS. You can learn how to create a server on AWS from [here](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html) and on GCP from [here](https://cloud.google.com/compute/docs/quickstart-linux). Or take a look at our simplified [docs](docs/vm.md).
-- Python 3.11 or higher. You can download it from [here](https://www.python.org/downloads/).
-- Docker with the Compose plugin. You can download Docker from [here](https://www.docker.com/products/docker-desktop) and learn how to use the Compose plugin from [here](https://docs.docker.com/compose/).
-- `Poetry` and `pyenv` installed on the machine. Installation steps for `Poetry` can be found [here](https://python-poetry.org/docs/#installation). For `pyenv` installation, please follow [these instructions](https://github.com/pyenv/pyenv?tab=readme-ov-file#installation).
-- An S3 bucket if you are using AWS or a GCS bucket if you are using GCP. This bucket is needed to store the `app.py` file, which contains your application's code. This setup allows you to make changes to your application without needing to SSH into the server and edit the `app.py` file in the directory with each change. You can learn how to create an S3 bucket from [here](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html) and a GCS bucket from [here](https://cloud.google.com/storage/docs/creating-buckets). Or take a look at our simplified [docs](docs/bucket.md).
-
-## How to start the application
-
-Once you have all the prerequisites, you can set up and deploy the environment by following these steps:
-
-1. **Clone the Repository**: Start by cloning this repository to your local machine using the command `git clone https://github.com/superlinked/superlinked`.
-
-1. **Navigate to the server folder**: Change your current directory to the cloned repository's `server/` directory using the command `cd <repo-directory>/server`. Replace `<repo-directory>` with the name of the directory where the repository was cloned.
-
-1. **Initialize the Virtual Environment**: Run the `init-venv.sh` script located in the `tools/` directory. This script will set up a Python virtual environment for the project. Use the command `./tools/init-venv.sh` to run the script.
-
-1. **Activate the Virtual Environment**: Source the environment that was just created by the `init-venv.sh` script. Enter the `runner/` directory and use the command `source "$(poetry env info --path)/bin/activate"` to activate the environment.
-
-1. **Configure the Application**: Edit the `config/config.yaml` file to include the bucket URI for `app.py`. The URI should end with `app.py`. This configuration allows the application to know where to find the `app.py` file in your bucket.
-    > **Note**: The default value is `local`. If this is set, the `app.py` location is automatically set to the `src/app.py` location inside the repository's directory.
-
-1. **Deploy the Application**: Finally, start the application by running the `deploy.py` script in the `tools/` directory with the `python tools/deploy.py up` command. This will deploy the application based on the configuration you provided in the previous step.
-
-This will start the environment and make it ready for use.
-
-Although if the AWS or GCP instance or your local setup has access to the target bucket the credential handling should be automatic, please note that you might need to configure your AWS or GCP credentials before you can use the environment. You can learn how to do this from [here](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-files.html) for AWS and [here](https://cloud.google.com/docs/authentication/getting-started) for GCP. The configuration files for these credentials can be used under the `config` directory if needed.
-
-### Checking the Status of the Services
-
-Once the application is deployed, you can check the status of the services by inspecting the Docker containers. The easiest way to look for logs is to use the built-in command provided by docker compose:
-```bash
-docker compose logs -f
-```
-
-If you would like to check the individual containers, use the `docker ps` command to list all running containers. This will show you the status of each service, including whether it's up and running.
-
-If you need more detailed information about a specific service, you can check its logs using the `docker logs <container-id>` command. Replace `<container-id>` with the ID of the container you're interested in. This will display the logs for that container, which can be useful for troubleshooting.
-
-### Resetting the Environment
-
-If something goes wrong and you need to reset the entire environment, you can do so with the following command from the `server/` directory:
-```bash
-python tools/deploy.py down && rm -rf cache/ && docker image rm -f server-executor server-poller
-```
-
-> Please note that that the name of the images may be different in your specific scenario, please adjust them according to your needs.
-
-This command will stop the application, remove the cache directory, and remove the Docker containers for the executor and the poller. After running this command, you can start the application again by following the steps in the [How to start the application](#how-to-start-the-application) section.
-
-## Usage
-
-There are two main parts of the system which you can interact with:
-- [Constructing the app.py](docs/app.md)
-- [Interacting with the application via the API](docs/api.md)
-
-To see the detailed description for each, click on the bullet points.
-
-## Support
-
-If you encounter any issues while using this environment, please open an issue in this repository and we will do our best to help you.
-
diff --git a/src/bonus_superlinked_rag/server/compose.yaml b/src/bonus_superlinked_rag/server/compose.yaml
deleted file mode 100644
index 759a688..0000000
--- a/src/bonus_superlinked_rag/server/compose.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-version: "3"
-
-services:
-  poller:
-    build:
-      context: runner
-      dockerfile: poller/Dockerfile
-    mem_limit: 256m
-    cpus: '0.5'
-    volumes:
-      - ./cache:/code/data
-      - ./config:/code/config
-      - ./src:/src
-
-  executor:
-    depends_on:
-      - poller
-      - redis
-    build:
-      context: runner
-      dockerfile: executor/Dockerfile
-    volumes:
-      - ./cache:/code/data
-    environment:
-      - APP_MODULE_PATH=data.src.app
-    ports:
-      - 8080:8080
-    env_file:
-      - runner/executor/.env
-
-  redis:
-    image: redis/redis-stack:latest
-    ports:
-      - "6379:6379"
-      - "8001:8001"
-    volumes:
-      - redis-data:/data
-
-volumes:
-  redis-data:
diff --git a/src/bonus_superlinked_rag/server/config/aws_credentials.json b/src/bonus_superlinked_rag/server/config/aws_credentials.json
deleted file mode 100644
index 22779cc..0000000
--- a/src/bonus_superlinked_rag/server/config/aws_credentials.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
-    "accessKeyId": <YOUR_ACCESS_KEY_ID>,
-    "region": <YOUR_REGION>,
-    "secretAccessKey": <YOUR_SECRET_ACCESS_KEY>
-}
\ No newline at end of file
diff --git a/src/bonus_superlinked_rag/server/config/config.yaml b/src/bonus_superlinked_rag/server/config/config.yaml
deleted file mode 100644
index 0dd68ba..0000000
--- a/src/bonus_superlinked_rag/server/config/config.yaml
+++ /dev/null
@@ -1 +0,0 @@
-app_location: local
diff --git a/src/bonus_superlinked_rag/server/config/gcp_credentials.json b/src/bonus_superlinked_rag/server/config/gcp_credentials.json
deleted file mode 100644
index 9e6b76e..0000000
--- a/src/bonus_superlinked_rag/server/config/gcp_credentials.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-    "type": "service_account",
-    "project_id": "PROJECT_ID",
-    "private_key_id": "KEY_ID",
-    "private_key": "-----BEGIN PRIVATE KEY-----\nPRIVATE_KEY\n-----END PRIVATE KEY-----\n",
-    "client_email": "SERVICE_ACCOUNT_EMAIL",
-    "client_id": "CLIENT_ID",
-    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-    "token_uri": "https://accounts.google.com/o/oauth2/token",
-    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-    "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/SERVICE_ACCOUNT_EMAIL"
-  }
\ No newline at end of file
diff --git a/src/bonus_superlinked_rag/server/docs/api.md b/src/bonus_superlinked_rag/server/docs/api.md
deleted file mode 100644
index c06ce38..0000000
--- a/src/bonus_superlinked_rag/server/docs/api.md
+++ /dev/null
@@ -1,87 +0,0 @@
-# Using the API to ingest and query the application
-
-Once you have your application up and running, you can start loading data and querying the API. Here's a step-by-step guide on how to do it:
-
-## Ingest an entry
-
-1. **Data Ingestion**: You can test data ingestion by making a POST request to the `/api/v1/ingest/your_schema` endpoint. Here's an example using `curl`:
-    ```bash
-    curl -X POST \
-        'http://localhost:8080/api/v1/ingest/your_schema' \
-        --header 'Accept: */*' \
-        --header 'Content-Type: application/json' \
-        --data-raw '{
-            "id": "your_id",
-            ...
-        }'
-    ```
-    > **Note**: The current example will not work, please change the request body as your schema requires it.
-
-## Query your system
-
-1. **Query the API**: After ingesting data, you can query the API by making a POST request to the `/api/v1/search/query` endpoint. Here's an example using `curl`:
-    ```bash
-    curl -X POST \
-        'http://localhost:8080/api/v1/search/query' \
-        --header 'Accept: */*' \
-        --header 'Content-Type: application/json' \
-        --data-raw '{
-            "query_text": "your_search_text"
-        }'
-    ```
-This request will search for entities that match the query text.
-
-## Load data from file(s)
-
-### See available data loaders
-
-To see what data loaders are available, send a request to the endpoint below:
-
-```bash
-curl 'http://localhost:8080/data-loader/'
-```
-Successful response (200 OK):
-```JSON
-{
-    "result": [
-        "<NAME_OF_YOUR_DATA_LOADER>": "DataLoaderConfig(path='https://path-to-your-file.csv', format=<DataFormat.CSV: 2>, name=None, pandas_read_kwargs='{sep: ;}')"
-    ]
-}
-```
-
-> The keys are the available data loader names that you can use for the rest of the data loader endpoints below. To see how these names are constructed and can be altered, read the [docs here](app.md#incorporate-data-source).
-
-### Trigger the data load
-
-To initiate the data load, invoke its endpoint. This will spawn an asynchronous task `DataLoaderSource` by its name as defined in your `app.py`. To trigger the endpoint, simply send a request with `curl` as shown below. The response should be 202 Accepted.
-If the name you provided is not found in the system, a 404 NOT FOUND will be returned. If you try to start it while it is already running, a 409 CONFLICT will be returned.
-
-```bash
-curl -X POST 'http://localhost:8080/data-loader/<NAME_OF_YOUR_DATA_LOADER>/run'
-```
-Successful response (200 OK):
-```JSON
-{
-    "result": "Background task successfully started with name: <NAME_OF_YOUR_DATA_LOADER>",
-}
-```
-
-### See the status of your tasks
-
-To check if your data is still being loaded to the system, you can trigger the status endpoint. The endpoint needs the name of your data loader. If you have multiple of them, you need to send one request for each task. The endpoint will return with 200 if the given task exists in the system and 404 if not. The response will be a JSON with a human readable explanation what happened. If it failed, you still get a 200 with details in the body.
-
-```bash
-curl -X GET 'http://localhost:8080/data-loader/<NAME_OF_YOUR_DATA_LOADER>/status
-```
-Successful response (200 OK):
-```JSON
-{
-    "result": "Task is still running",
-}
-```
-If the data loader is not found by its name in the system (404 Not found):
-```JSON
-{
-    "result": "Task not found with name: <NAME_OF_YOUR_DATA_LOADER>",
-}
-```
diff --git a/src/bonus_superlinked_rag/server/docs/app.md b/src/bonus_superlinked_rag/server/docs/app.md
deleted file mode 100644
index 92b1748..0000000
--- a/src/bonus_superlinked_rag/server/docs/app.md
+++ /dev/null
@@ -1,205 +0,0 @@
-# How to construct the app.py file
-
-The application's main logic resides in the `app.py` file. This is where you define your application's structure and behavior using the Superlinked library.
-
-By default, all examples within this documentation utilize an in-memory database. This configuration is optimal for testing and initial experimentation with the Superlinked framework. For detailed instructions on configuring and employing alternative vector databases, please refer to the [vector databases documentation.](vector_databases.md)
-
-To start using the system, you can use the example application located [here](example/app.py).
-You can find more complex examples how to construct the spaces and the queries in the [Superlinked notebooks](https://github.com/superlinked/superlinked/tree/main)
-
-> Important Note: The `RecencySpace` feature is turned off by default due to the constraints of this release. For a detailed explanation and instructions on enabling it, refer to the [Using Recency Space](#using-recency-space) section of the documentation.
-
-> Note: The primary aim of this document is to guide you on how to operate the Superlinked system with your preferred configuration, rather than explaining the inner workings of the Superlinked components. For a deeper understanding of the components, please refer to the notebooks mentioned above.
-
-## Understanding the building blocks of the application
-
-Here is a basic structure of an `app.py` file:
-```python
-# linked-file:dummy_app.py
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import RestQuery
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.in_memory_vector_database import InMemoryVectorDatabase
-
-
-@schema
-class YourSchema:
-    id: IdField
-    attribute: String
-
-
-your_schema = YourSchema()
-
-text_space = TextSimilaritySpace(text=your_schema.attribute, model="model-name")
-
-index = Index(text_space)
-
-query = (
-    Query(index)
-    .find(your_schema)
-    .similar(
-        text_space.text,
-        Param("query_text"),
-    )
-)
-
-your_source: RestSource = RestSource(your_schema)
-
-executor = RestExecutor(
-    sources=[your_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-)
-
-SuperlinkedRegistry.register(executor)
-```
-
-In this example, we define a schema for our data, create a text similarity space, define an index, and set up a query. We then create a REST source and an executor that uses this source, the index, and the query.
-
-
-You can find more detailed information and examples of various features in the [Superlinked feature notebooks](https://github.com/superlinked/superlinked/tree/main/notebook/feature). The [basic_building_blocks.ipynb](https://github.com/superlinked/superlinked/blob/main/notebook/feature/basic_building_blocks.ipynb) notebook provides a comprehensive guide on the basic structure and how to use it, while the other notebooks cover various features of the Superlinked library.
-
-In this deployment setup, you are not required to define any computations as you would in the [basic_building_blocks.ipynb](https://github.com/superlinked/superlinked/blob/main/notebook/feature/basic_building_blocks.ipynb) notebook. Instead, your focus will be on defining the schema, the text similarity space, the index, the query, the REST source, and the executor.
-
-The `app.py` file ends with adding the executor to the `SuperlinkedRegistry`. Use SuperlinkedRegistry to register your components for production, this is not required when experimenting with InMemoryExecutor. If you've got more than one executor, you can add them all at once, separated by commas, like this: `SuperlinkedRegistry.register(executor1, executor2, executor3)`. 
-The executor is the component that executes the queries defined in your application, utilizing the sources, indices, and queries you have established. Here's an example of how to define an executor:
-```python
-executor = RestExecutor(
-    sources=[your_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-)
-
-SuperlinkedRegistry.register(executor)
-```
-In this example, `your_source` is the REST source you've defined, `index` is the index you've created, and `query` is the query you've set up. The `RestQuery` function wraps your query with a `RestDescriptor`, which provides a name for your query that can be used to call it from the REST API. In the preceding example, the path is set to `/api/v1/search/query`. This is where you assign a name to the final segment of the path, assuming you are using the default settings. [More detailed API info](#customize-your-api)
-
-This setup abstracts away the need for explicit computation definition, allowing you to concentrate on defining your application's structure and behavior. The Superlinked library handles the rest, executing your queries and returning the results when the application is run. This approach allows you to make changes to your application without needing to SSH into the server and edit the `app.py` file in the directory with each change.
-
-Remember to replace the placeholders in the example with your actual data and requirements. The `app.py` file should be stored in an S3 bucket (if you're using AWS) or a GCS bucket (if you're using GCP). This setup allows you to make changes to your application without needing to SSH into the server and edit the `app.py` file in the directory with each change. 
-
-## Configuring the data loader
-
-The system has a feature to load data from file(s) either from local or remote.
-
-> Note: In the absence of specified chunking, the loader will attempt to read and load the entire file into the system by default. Mind your memory! If possible, utilize file formats that support chunking and include the necessary parameters in the `pandas_read_kwargs` as indicated below.
-
-Constraints: 
-- When running your preview locally, only local files or public ones from remote sources can be used. Targeting an S3 bucket or GCP that requires authentication is not possible.
-- When running in the cloud, for example on GCP, you can target private Google Cloud Storage (GCS) bucket but only those that the Google Cloud Engine (GCE) instance has access to. 
-  It will utilize its own authentication and authorization, but no other private cloud sources like S3 can be used. Local files on the GCE or any public file that doesn't require authorization can also be used.
-
-### Incorporate Data Source
-
-Create a specific source that can point to a local or a remote file. This file can be parsed and loaded into the system more efficiently than invoking the REST endpoint for each piece of data:
-```python
-# The path can be a local file, a remote. The available DataFormats are: [JSON, CSV, PARQUET, ORC, XML, FWF]
-# The `name_of_your_loader` is an optional parameter, which is the identifier of your loader. Read more about it below the code block.
-# The last argument is a pass through argument that pandas should be able to use so use the format that is compatible with pandas.
-# Note: the pandas_read_kwargs is an optional parameter, if you don't need any customization, it will use the defaults.
-config = DataLoaderConfig("https://path-to-your-file.csv", DataFormat.CSV, "name_of_your_loader", pandas_read_kwargs={"sep": ";"})
-data_loader_source = DataLoaderSource(your_schema, config) # Add your config to the source. This is mandatory.
-
-executor = RestExecutor(
-    sources=[your_source, data_loader_source], # Incorporate the data_loader_source into the sources here.
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-)
-```
-
-> Name of your data loader: The `name` parameter in `DataLoaderConfig` is optional. By default, it adopts the snake_case version of your schema's name used in `DataLoaderSource`. If you have multiple data loaders for the same schema or prefer a different name, simply set the `name` parameter accordingly.
-> Note that the name will always be converted to snake_case. To see the configured data loaders in your system, refer to the [API documentation](api.md#see-available-data-loaders).
-
-The data loader is now configured but **it only runs if you send a request to the data loader endpoint!** To see how to trigger it, check the API documentation [here](api.md#trigger-the-data-load)
-
-## Optional steps
-
-### Schema to column mappings
-
-By default, the system will attempt to parse your file, hence the column names should align with your schema attributes. If an `id` column has a different name for example, as well as the other schema fields, it needs to be mapped to the schema you are attempting to load. To map field names to your schema, utilize the data parser as shown below:
-```python
-# Instantiate a DataFrameParser object, composed of the schema you wish to map and the actual mapping. The format for mapping is: `<schema.field>: <column_name>`
-# Note: If the column names are exactly the same (case sensitive) as your schema, you don't need to provide a parser for the source at all.
-data_frame_parser = DataFrameParser(your_schema, mapping={your_schema.id: "id_field_name", your_schema.attribute: "custom_field_name"})
-data_loader_source = DataLoaderSource(your_schema, config, data_frame_parser) # Incorporate the parser into your source
-```
-
-### Data Chunking
-
-Data chunking allows you to load more data than your memory could typically handle at once. This is particularly beneficial when dealing with data sets that span multiple gigabytes.
-> If you're uncertain whether your data will fit into your memory, it's strongly advised to employ chunking to prevent unexpected problems. By setting the [log level to debug in the executor](../runner/executor/.env), you can view pandas memory information regardless of whether you're chunking the data. This assists in estimating memory usage.
-
-To implement chunking, you'll need to use either CSV or JSON formats (specifically JSONL, which includes JSON objects on each line).
-
-Here's an example of what a chunking configuration might look like:
-```python
-# For CSV
-config = DataLoaderConfig("https://path-to-your-file.csv", DataFormat.CSV, pandas_read_kwargs={"chunksize": 10000})
-# For JSON
-config = DataLoaderConfig("https://path-to-your-file.jsonl", DataFormat.JSON, pandas_read_kwargs={"lines": True, "chunksize": 10000})
-```
-
-The Superlinked library performs internal batching for embeddings, with a default batch size of 10000. If you are utilizing a chunk size different from 10000, it is advisable to adjust this batch size to match your chunk size.
-To modify this, alter the `INMEMORY_PUT_CHUNK_SIZE` value [in this file](../runner/executor/.env)
-
-### Customize your API
-
-If you want to configure your API path, you can do that with the `RestEndpointConfiguration`, which can alter your URL. By default the API looks like:
-- Query endpoint's path is: `/api/v1/search/<query_name>` which aligns with the schema: `/<api_root_path>/<query_path_prefix>/<query_name>` 
-- Data ingestion endpoint's path is: `/api/v1/ingest/<schema_name>` which aligns with the schema: `/<api_root_path>/<ingest_path_prefix>/<schema_name>`
-- The rest of the API is non configurable, that is part of the so called, management API.
-
-To change the API's default path, see the following code, that let's you customize it:
-```python
-rest_endpoint_config = RestEndpointConfiguration(
-    query_path_prefix="retrieve",
-    ingest_path_prefix="insert",
-    api_root_path="/superlinked/v3",
-) # This will change the root path for both ingest and query endpoints
-
-executor = RestExecutor(
-    sources=[your_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-    rest_endpoint_config=rest_endpoint_config # Incorporate your config here
-)
-```
-
-### Using Recency Space
-
-Recency Space has two current limitations:
-- Recency embeddings become outdated over time as they are not recalculated periodically. Our encoder only needs a constant number of updates for this to work correctly, but that update mechanism has not been open-sourced yet - coming soon!
-- At server startup, the application captures the server's current UTC timestamp as `now`. During development, each modification and restart of the application will result in a new timestamp, which makes results irreproducible within restarts.
-
-The first one is a known limitation that will be fixed in the near future. The second one can be solved with setting the timestamp to a fixed value.
-```python
-NOW = int(datetime(year=2024, month=1, day=2, tzinfo=UTC).timestamp()) # Here you can set the exact date to work with. Note: you can set hours, minute and so on.
-EXECUTOR_DATA = {CONTEXT_COMMON: {CONTEXT_COMMON_NOW: NOW}} # Then use the following dict structure
-```
-
-Then add the `EXECUTOR_DATA` to your executor, like:
-
-```python
-executor = RestExecutor(
-    sources=[source], indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-    context_data=EXECUTOR_DATA, # Add your executor data here
-)
-```
-
-Finally, you need to set a flag to prevent exceptions when utilizing Recency Space. Set the `DISABLE_RECENCY_SPACE` flag to `false` in the [.env config file](../runner/executor/.env)
-
diff --git a/src/bonus_superlinked_rag/server/docs/bucket.md b/src/bonus_superlinked_rag/server/docs/bucket.md
deleted file mode 100644
index 32471c0..0000000
--- a/src/bonus_superlinked_rag/server/docs/bucket.md
+++ /dev/null
@@ -1,57 +0,0 @@
-## What are Storage Buckets?
-
-Storage buckets are fundamental entities used to store data in cloud storage services. They are essentially containers for data where you can upload, download, and manage files. Two of the most popular cloud storage services are Amazon S3 (Simple Storage Service) and Google Cloud Storage (GCS).
-
-### Amazon S3
-
-Amazon S3 buckets are used to store objects, which consist of data and its descriptive metadata. They are globally unique, and defined at the Universal Resource Locator (URL) level.
-
-### Google Cloud Storage (GCS)
-
-Google Cloud Storage buckets are similar to Amazon S3 buckets. They are used to store data objects in Google Cloud. Each bucket is associated with a specific project, and you can choose to make the bucket data public or private.
-
-## Purpose in Our System
-
-In our system, we use these storage buckets to store user code. When you write and save your code, it gets stored in either an S3 or GCS bucket. This allows us to securely store your code, retrieve it when needed, and even share it among multiple services or instances if required.
-
-## How to Create Buckets
-
-### Creating an Amazon S3 Bucket using AWS CLI
-
-To create an Amazon S3 bucket, you can use the AWS CLI (Command Line Interface). Here's how:
-
-1. First, install the AWS CLI on your machine. You can find the installation instructions [here](https://aws.amazon.com/cli/).
-2. Configure the AWS CLI with your credentials:
-
-```bash
-aws configure
-```
-
-1. Create a new S3 bucket:
-
-```bash
-aws s3api create-bucket --bucket my-bucket-name --region us-west-2
-```
-
-Replace `my-bucket-name` with your desired bucket name and `us-west-2` with the AWS region you want to create your bucket in. For more information on S3 bucket creation, refer to the [AWS documentation](https://docs.aws.amazon.com/AmazonS3/latest/userguide/create-bucket-overview.html).
-
-### Creating a Google Cloud Storage Bucket using `gsutil`
-
-To create a Google Cloud Storage bucket, you can use the `gsutil` tool. Here's how:
-
-1. First, install the Google Cloud SDK, which includes the `gsutil` tool. You can find the installation instructions [here](https://cloud.google.com/sdk/docs/install).
-2. Authenticate your account:
-
-```bash
-gcloud auth login
-```
-
-1. Create a new GCS bucket:
-
-```bash
-gsutil mb -p project_id -c storage_class -l location gs://my-bucket-name
-```
-
-Replace `project_id` with the ID of your project, `storage_class` with the desired storage class for the bucket, `location` with the desired bucket location and `my-bucket-name` with your desired bucket name. For more information on GCS bucket creation, refer to the [Google Cloud Documentation](https://cloud.google.com/storage/docs/creating-buckets).
-
-Remember, the bucket names must be unique across all existing bucket names in Amazon S3 or Google Cloud Storage.
diff --git a/src/bonus_superlinked_rag/server/docs/dummy_app.py b/src/bonus_superlinked_rag/server/docs/dummy_app.py
deleted file mode 100644
index 3064d6c..0000000
--- a/src/bonus_superlinked_rag/server/docs/dummy_app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import RestQuery
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.in_memory_vector_database import InMemoryVectorDatabase
-
-
-@schema
-class YourSchema:
-    id: IdField
-    attribute: String
-
-
-your_schema = YourSchema()
-
-text_space = TextSimilaritySpace(text=your_schema.attribute, model="model-name")
-
-index = Index(text_space)
-
-query = (
-    Query(index)
-    .find(your_schema)
-    .similar(
-        text_space.text,
-        Param("query_text"),
-    )
-)
-
-your_source: RestSource = RestSource(your_schema)
-
-executor = RestExecutor(
-    sources=[your_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-)
-
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/docs/example/amazon_app.py b/src/bonus_superlinked_rag/server/docs/example/amazon_app.py
deleted file mode 100644
index 2c3f799..0000000
--- a/src/bonus_superlinked_rag/server/docs/example/amazon_app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from datetime import datetime, timedelta, timezone
-
-from superlinked.framework.common.dag.context import CONTEXT_COMMON, CONTEXT_COMMON_NOW
-from superlinked.framework.common.dag.period_time import PeriodTime
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import Integer, String, StringList, Timestamp
-from superlinked.framework.dsl.executor.rest.rest_configuration import (
-    RestQuery,
-)
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.data_loader_source import DataFormat, DataLoaderConfig, DataLoaderSource
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.categorical_similarity_space import (
-    CategoricalSimilaritySpace,
-)
-from superlinked.framework.dsl.space.number_space import Mode, NumberSpace
-from superlinked.framework.dsl.space.recency_space import RecencySpace
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.in_memory_vector_database import InMemoryVectorDatabase
-
-START_OF_2024_TS = int(datetime(2024, 1, 2, tzinfo=timezone.utc).timestamp())
-EXECUTOR_DATA = {CONTEXT_COMMON: {CONTEXT_COMMON_NOW: START_OF_2024_TS}}
-
-
-@schema
-class Review:
-    rating: Integer
-    timestamp: Timestamp
-    verified_purchase: StringList
-    review_text: String
-    id: IdField
-
-
-review = Review()
-
-
-rating_space = NumberSpace(review.rating, min_value=1, max_value=5, mode=Mode.MAXIMUM)
-recency_space = RecencySpace(review.timestamp, period_time_list=PeriodTime(timedelta(days=3650)))
-verified_category_space = CategoricalSimilaritySpace(
-    review.verified_purchase,
-    categories=["True", "False"],
-    uncategorized_as_category=False,
-    negative_filter=-5,
-)
-relevance_space = TextSimilaritySpace(review.review_text, model="sentence-transformers/all-mpnet-base-v2")
-
-index = Index([relevance_space, recency_space, rating_space, verified_category_space])
-
-source: RestSource = RestSource(review)
-
-
-query = (
-    Query(
-        index,
-        weights={
-            rating_space: Param("rating_weight"),
-            recency_space: Param("recency_weight"),
-            verified_category_space: Param("verified_category_weight"),
-            relevance_space: Param("relevance_weight"),
-        },
-    )
-    .find(review)
-    .similar(relevance_space.text, Param("query_text"))
-    .similar(verified_category_space.category, Param("query_verified"))
-)
-
-config = DataLoaderConfig(
-    "https://storage.googleapis.com/superlinked-preview-test-data/amazon_dataset_1000.jsonl",
-    DataFormat.JSON,
-    pandas_read_kwargs={"lines": True, "chunksize": 100},
-)
-loader_source: DataLoaderSource = DataLoaderSource(review, config)
-
-executor = RestExecutor(
-    sources=[source, loader_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-    context_data=EXECUTOR_DATA,
-)
-
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/docs/example/app.py b/src/bonus_superlinked_rag/server/docs/example/app.py
deleted file mode 100644
index 7e8a9d3..0000000
--- a/src/bonus_superlinked_rag/server/docs/example/app.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import (
-    RestQuery,
-)
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.in_memory_vector_database import InMemoryVectorDatabase
-
-
-@schema
-class CarSchema:
-    id: IdField
-    make: String
-    model: String
-
-
-car_schema = CarSchema()
-
-car_make_text_space = TextSimilaritySpace(text=car_schema.make, model="all-MiniLM-L6-v2")
-car_model_text_space = TextSimilaritySpace(text=car_schema.model, model="all-MiniLM-L6-v2")
-
-index = Index([car_make_text_space, car_model_text_space])
-
-query = (
-    Query(index)
-    .find(car_schema)
-    .similar(car_make_text_space.text, Param("make"))
-    .similar(car_model_text_space.text, Param("model"))
-    .limit(Param("limit"))
-)
-
-car_source: RestSource = RestSource(car_schema)
-
-executor = RestExecutor(
-    sources=[car_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=InMemoryVectorDatabase(),
-)
-
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/docs/mongodb/app_with_mongodb.py b/src/bonus_superlinked_rag/server/docs/mongodb/app_with_mongodb.py
deleted file mode 100644
index da66bc1..0000000
--- a/src/bonus_superlinked_rag/server/docs/mongodb/app_with_mongodb.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import (
-    RestQuery,
-)
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.mongo_vector_database import MongoVectorDatabase
-
-
-@schema
-class CarSchema:
-    id: IdField
-    make: String
-    model: String
-
-
-car_schema = CarSchema()
-
-car_make_text_space = TextSimilaritySpace(text=car_schema.make, model="all-MiniLM-L6-v2")
-car_model_text_space = TextSimilaritySpace(text=car_schema.model, model="all-MiniLM-L6-v2")
-
-index = Index([car_make_text_space, car_model_text_space])
-
-query = (
-    Query(index)
-    .find(car_schema)
-    .similar(car_make_text_space.text, Param("make"))
-    .similar(car_model_text_space.text, Param("model"))
-    .limit(Param("limit"))
-)
-
-car_source: RestSource = RestSource(car_schema)
-
-mongo_vector_database = MongoVectorDatabase(
-    "<USER>:<PASSWORD>@<HOST_URL>",
-    "<DATABASE_NAME>",
-    "<CLUSTER_NAME>",
-    "<PROJECT_ID>",
-    "<API_PUBLIC_KEY>",
-    "<API_PRIVATE_KEY>",
-)
-
-executor = RestExecutor(
-    sources=[car_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=mongo_vector_database,
-)
-
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/docs/mongodb/mongodb.md b/src/bonus_superlinked_rag/server/docs/mongodb/mongodb.md
deleted file mode 100644
index 4698921..0000000
--- a/src/bonus_superlinked_rag/server/docs/mongodb/mongodb.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# MongoDB
-
-This document provides clear steps on how to use and integrate MongoDB with Superlinked.
-
-## Configuring your existing managed MongoDB
-
-To integrate MongoDB with Superlinked, ensure you are using a version that supports Atlas Vector Search capabilities. Refer to the MongoDB documentation for [more information](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/).
-
-Superlinked requires access to MongoDB to list, create, and delete Atlas Search Indexes. As of writing, MongoDB separates functionality by database instance sizes. If you use anything below M10, the database does not support creating, listing, and deleting the Atlas Search Index via a standard user, only via the administration API. You can read more [about the limitation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-type/) and also [about the administration API](https://www.mongodb.com/docs/atlas/configure-api-access/). To support all types, Superlinked uses the aforementioned API to manage the indexes.
-
-Due to the reasons above, an API key with the `Project Data Access Admin` role is required. More about how to create that can be found [below](#start-a-managed-mongo-db-instance).
-
-> Note: When using that API, you will need `project_id` and `cluster_name`, which are also described [below](#start-a-managed-mongo-db-instance) on how to find this information.
-
-## Modifications in app.py
-
-To integrate MongoDB, you need to add the `MongoVectorDatabase` class and include it in the executor. Here’s how you can do it:
-```python
-from superlinked.framework.dsl.storage.mongo_vector_database import MongoVectorDatabase
-
-vector_database = MongoVectorDatabase(
-    host="<USER>:<PASSWORD>@<HOST_URL>", # The DB's host URL with the username and password
-    db_name="<DATABASE_NAME>", # Name of your database inside your cluster. You need to create it, the system won't do it automatically
-    cluster_name="<CLUSTER_NAME>", # Name of your cluster inside your project
-    project_id="<PROJECT_ID>", # The ID (not the name) of your project. To see how to find it, read the note below this box
-    admin_api_user="<API_USER>", # The generated API key's user, which called public key on Mongo Atlas
-    admin_api_password="<API_PASSWORD>", # The API password, which generated by mongo, they reference it on Atlas as private key
-    # Anything else is handled as kwargs so those will be passed in to the MongoClient. Read more about the possible parameters below
-)
-```
-
-> Project ID: To find your Project ID, select you organization in the top left corner of Atlas UI. Afterward, find your project (don't click on it). In the last column ("Actions") expend the menu by clicking on the ellipses (...), then copy select "Copy Project ID" which will paste it to your clipboard.
-
-Alternative: Click on your project on Atlas and in the URL you will find the id: `https://cloud.mongodb.com/v2/12755aca606daa697d3e30b9#/overview` where the `12755aca606daa697d3e30b9` before the `#` and after the `https://cloud.mongodb.com/v2/` is your project ID. The organization ID is very similar to this number, but please make sure that you copy the ID after you selected the project!
-
-> Extra parameters: Extra params can be passed in to the PyMongo client called MongoClient. Please read the [documentation](https://pymongo.readthedocs.io/en/stable/api/pymongo/mongo_client.html#pymongo.mongo_client.MongoClient) for more information.
-
-Once you have configured the `MongoVectorDatabase`, set it as your `vector_database` in the `RestExecutor`:
-```python
-...
-executor = RestExecutor(
-    sources=[source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=vector_database, # Or any variable that you assigned your `MongoVectorDatabase`
-)
-...
-```
-
-## Start a managed Mongo DB instance
-
-A step-by-step guide to set up a database, a user, and the required API key.
-
-### Creating the Database:
-1. Navigate to [Mongo Atlas](https://cloud.mongodb.com/) and sign in.
-2. Create your cluster. The cluster name will be needed for the configuration mentioned above. You can choose any other options as they do not impact Superlinked's functionality.
-3. Click on the `Database` option in the left menu column.
-4. Once the cluster is created, click on its name and then go to the collections tab or click on the `Browse Collections` button.
-5. Click on `Add My Own Data` and provide a name for your database and collection. The database name will be required for the configuration above. The collection name is not critical and can be deleted later as Superlinked will create its own.
-
-### Creating a Database User and add your IP:
-1. Click on the `Database` option on the left.
-2. Click the `Connect` button next to your cluster's name.
-3. In the pop-up window:
-   1. Click on the `Allow access from Anywhere` or select the `Add a different IP address` and insert your VM's or local IP address.
-   2. Enter the username and password for your user. These credentials will be needed for the configuration above.
-
-### Creating the API Key:
-1. Click on the `Access Manager` selector at the top left corner next to your organization selector and select your project.
-2. Go to the `API Keys` tab.
-3. Provide a name for the API key and select the `Project Data Access Admin` role in the `Project Permissions` selector.
-4. Copy the `Private Key` as it will not be accessible again. The `Public key` and `Private key` will be your `admin_api_user` and `admin_api_password` in your connection in this order.
-
-
-## Example app with Mongo DB
-
-You can find an example that utilizes Mongo DB [here](app_with_mongodb.py)
-
diff --git a/src/bonus_superlinked_rag/server/docs/redis/app_with_redis.py b/src/bonus_superlinked_rag/server/docs/redis/app_with_redis.py
deleted file mode 100644
index 37a3ac2..0000000
--- a/src/bonus_superlinked_rag/server/docs/redis/app_with_redis.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import (
-    RestQuery,
-)
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.text_similarity_space import TextSimilaritySpace
-from superlinked.framework.dsl.storage.redis_vector_database import RedisVectorDatabase
-
-
-@schema
-class CarSchema:
-    id: IdField
-    make: String
-    model: String
-
-
-car_schema = CarSchema()
-
-car_make_text_space = TextSimilaritySpace(text=car_schema.make, model="all-MiniLM-L6-v2")
-car_model_text_space = TextSimilaritySpace(text=car_schema.model, model="all-MiniLM-L6-v2")
-
-index = Index([car_make_text_space, car_model_text_space])
-
-query = (
-    Query(index)
-    .find(car_schema)
-    .similar(car_make_text_space.text, Param("make"))
-    .similar(car_model_text_space.text, Param("model"))
-    .limit(Param("limit"))
-)
-
-car_source: RestSource = RestSource(car_schema)
-
-redis_vector_database = RedisVectorDatabase("<HOST_URL>", 12345, username="default", password="<password>")
-
-executor = RestExecutor(
-    sources=[car_source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=redis_vector_database,
-)
-
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/docs/redis/redis.md b/src/bonus_superlinked_rag/server/docs/redis/redis.md
deleted file mode 100644
index 0b8be51..0000000
--- a/src/bonus_superlinked_rag/server/docs/redis/redis.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Redis
-
-This document provides clear steps on how to use and integrate Redis with Superlinked.
-
-## Configuring your existing managed Redis
-
-To use Superlinked with Redis, you will need several Redis modules. The simplest approach is to use the official Redis Stack, which includes all the necessary modules. Installation instructions for the Redis Stack can be found in the [Redis official documentation](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/). Alternatively, you can start a managed instance provided by Redis (a free-tier is available). For detailed steps on initiating a managed instance, refer to the [Start a Managed Redis Instance](#start-a-managed-redis-instance) section below.
-
-Once your Redis instance is up and running, ensure it is accessible from the server that will use it. Additionally, configure the necessary authentication settings as described below.
-
-## Modifications in app.py
-
-To integrate Redis, you need to add the `RedisVectorDatabase` class and include it in the executor. Here’s how you can do it:
-
-To configure your Redis, the following code will help you:
-```python
-from superlinked.framework.dsl.storage.redis_vector_database import RedisVectorDatabase
-
-vector_database = RedisVectorDatabase(
-    "<your_redis_host>", # (Mandatory) This is your redis URL without any port or extra fields
-    12315, # (Mandatory) This is the port and it should be an integer
-    # These params must be in a form of kwarg params. Here you can specify anything that the official python client 
-    # enables. The params can be found here: https://redis.readthedocs.io/en/stable/connections.html. Below you can see a very basic user-pass authentication as an example.
-    username="test",
-    password="password"
-)
-```
-
-Once you have configured the vector database just simply set it as your vector database.
-```python
-...
-executor = RestExecutor(
-    sources=[source],
-    indices=[index],
-    queries=[RestQuery(RestDescriptor("query"), query)],
-    vector_database=vector_database, # Or any variable that you assigned your `RedisVectorDatabase`
-)
-...
-```
-
-## Start a Managed Redis Instance
-
-To initiate a managed Redis instance, navigate to [Redis Labs](https://app.redislabs.com/), sign in, and click the "New Database" button. On the ensuing page, locate the `Type` selector, which offers two options: `Redis Stack` and `Memcached`. By default, `Redis Stack` is pre-selected, which is the correct choice. If it is not selected, ensure to choose `Redis Stack`. For basic usage, no further configuration is necessary. Redis already generated a user which called `default` and a password that you can see below it. However, if you intend to use the instance for persistent data storage beyond sandbox purposes, consider configuring High Availability (HA), data persistence, and other relevant settings.
-
-## Example app with Redis
-
-You can find an example that utilizes Redis [here](app_with_redis.py)
-
diff --git a/src/bonus_superlinked_rag/server/docs/vector_databases.md b/src/bonus_superlinked_rag/server/docs/vector_databases.md
deleted file mode 100644
index 4d9b882..0000000
--- a/src/bonus_superlinked_rag/server/docs/vector_databases.md
+++ /dev/null
@@ -1,8 +0,0 @@
-# Vector databases
-
-This document will list and point to the detailed documentation of the supported vector databases:
-
-- [Redis](redis/redis.md)
-- [MongoDB](mongodb/mongodb.md)
-
-Missing your favorite one? [Let us know in github discussions](https://github.com/superlinked/superlinked/discussions/41)
diff --git a/src/bonus_superlinked_rag/server/docs/vm.md b/src/bonus_superlinked_rag/server/docs/vm.md
deleted file mode 100644
index 5736d75..0000000
--- a/src/bonus_superlinked_rag/server/docs/vm.md
+++ /dev/null
@@ -1,47 +0,0 @@
-## What are Virtual Machines?
-
-Virtual Machines (VMs) are emulations of computer systems. They can run applications and services on a virtual platform, as if they were on a physical machine, while sharing the underlying hardware resources.
-
-### VM size recommendations
-
-The minimum VM size is `t3.small` in AWS or `e2-small` in GCP. Both configurations offer 2 vCPUs and 2GB of RAM. Note that if you are utilizing in-memory storage, the 2GB memory capacity can only accommodate a few tens of thousands of records. For larger datasets, opt for a machine with more RAM. The data loader functionality is CPU-intensive, so if you plan to use it, consider provisioning additional vCPUs.
-
-The system comprises two components: poller and executor. The poller is a basic service, constrained in the Docker Compose file to use only 256MB of memory and 0.5 vCPU. The executor, which runs Superlinked along with a few minor additions, will consume the majority of the resources. It has no set limits in the Docker Compose file, so any resources not utilized by the poller will be allocated to the executor.
-
-### Amazon EC2
-
-Amazon Elastic Compute Cloud (EC2) is a part of Amazon's cloud-computing platform, Amazon Web Services (AWS). EC2 allows users to rent virtual computers on which to run their own computer applications.
-
-### Google Cloud Compute Engine
-
-Google Cloud Compute Engine delivers virtual machines running in Google's innovative data centers and worldwide fiber network. Compute Engine's tooling and workflow support enable scaling from single instances to global, load-balanced cloud computing.
-
-## Creating an Amazon EC2 Instance using AWS CLI
-
-To create an Amazon EC2 instance, you can use the AWS CLI. Here's how:
-
-1. Install and configure the AWS CLI as described in the previous section.
-2. Launch an EC2 instance:
-
-```bash
-aws ec2 run-instances --image-id ami-0abcdef1234567890 --count 1 --instance-type t3.small --key-name MyKeyPair --security-group-ids sg-903004f8 --subnet-id subnet-6e7f829e
-```
-
-Replace the `image-id`, `key-name`, `security-group-ids`, and `subnet-id` with your own values. The `image-id` is the ID of the AMI (Amazon Machine Image), `key-name` is the name of the key pair for the instance, `security-group-ids` is the ID of the security group, and `subnet-id` is the ID of the subnet.
-
-For more information on creating key pairs, refer to the [AWS documentation](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html#having-ec2-create-your-key-pair). For a list of available regions, refer to the [AWS Regional Services List](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/).
-
-## Creating a Google Cloud Compute Engine VM using gcloud
-
-To create a Google Cloud Compute Engine VM, you can use the `gcloud` command-line tool. Here's how:
-
-1. Install and authenticate the Google Cloud SDK as described in the previous section.
-2. Create a Compute Engine instance:
-
-```bash
-gcloud compute instances create my-vm --machine-type=e2-small --image-project=debian-cloud --image-family=debian-9 --boot-disk-size=50GB
-```
-
-Replace `my-vm` with your desired instance name. The `machine-type` is the machine type of the VM, `image-project` is the project ID of the image, `image-family` is the family of the image, and `boot-disk-size` is the size of the boot disk. Please use a `boot-disk-size` value over 20 GB.
-
-Remember, you need to have the necessary permissions and quotas to create VM instances in both Amazon EC2 and Google Cloud Compute Engine.
diff --git a/src/bonus_superlinked_rag/server/runner/.python-version b/src/bonus_superlinked_rag/server/runner/.python-version
deleted file mode 100644
index 2c07333..0000000
--- a/src/bonus_superlinked_rag/server/runner/.python-version
+++ /dev/null
@@ -1 +0,0 @@
-3.11
diff --git a/src/bonus_superlinked_rag/server/runner/executor/Dockerfile b/src/bonus_superlinked_rag/server/runner/executor/Dockerfile
deleted file mode 100644
index 004d018..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-# --- Python base ---
-
-FROM python:3.11.9 AS python-base
-
-ENV PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    \
-    PIP_NO_CACHE_DIR=off \
-    PIP_DISABLE_PIP_VERSION_CHECK=on \
-    PIP_DEFAULT_TIMEOUT=100 \
-    \
-    POETRY_VERSION=1.8.2 \
-    POETRY_HOME="/opt/poetry" \
-    POETRY_VIRTUALENVS_IN_PROJECT=true \
-    POETRY_NO_INTERACTION=1 \
-    \
-    PYSETUP_PATH="/opt/pysetup" \
-    VENV_PATH="/opt/pysetup/.venv"
-
-ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
-# --- Builder base ---
-
-FROM python-base AS builder-base
-RUN apt-get update \
-    && apt-get install --no-install-recommends -y \
-    curl \
-    build-essential \
-    && rm -rf /var/lib/apt/lists/*
-
-SHELL ["/bin/bash", "-o", "pipefail", "-c"]
-RUN curl -sSL https://install.python-poetry.org | python -
-
-# --- Executor base ---
-
-FROM builder-base as executor-base
-
-WORKDIR $PYSETUP_PATH
-
-COPY poetry.lock pyproject.toml $PYSETUP_PATH/
-RUN poetry install --no-root --only executor
-
-# --- Executor ---
-
-FROM python:3.11.5-slim AS executor
-
-COPY --from=executor-base /opt/pysetup/.venv/ /opt/pysetup/.venv/
-
-WORKDIR /code
-
-RUN apt-get update && apt-get install --no-install-recommends -y supervisor && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-COPY executor/supervisord.conf /etc/supervisor/conf.d/supervisord.conf
-COPY executor/.env /code/executor/
-COPY executor/app /code/executor/app
-
-EXPOSE 8080
-ENTRYPOINT ["/usr/bin/supervisord", "-n"]
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/configuration/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/configuration/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/configuration/app_config.py b/src/bonus_superlinked_rag/server/runner/executor/app/configuration/app_config.py
deleted file mode 100644
index f5571fb..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/configuration/app_config.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-
-class AppConfig(BaseSettings):
-    SERVER_URL: str
-    APP_MODULE_PATH: str
-    LOG_LEVEL: str
-    PERSISTENCE_FOLDER_PATH: str
-    DISABLE_RECENCY_SPACE: bool
-
-    model_config = SettingsConfigDict(env_file="executor/.env", extra="ignore")
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/dependency_register.py b/src/bonus_superlinked_rag/server/runner/executor/app/dependency_register.py
deleted file mode 100644
index a3bdfb4..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/dependency_register.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from xmlrpc.client import ServerProxy
-
-import inject
-
-from executor.app.configuration.app_config import AppConfig
-from executor.app.service.data_loader import DataLoader
-from executor.app.service.file_handler_service import FileHandlerService
-from executor.app.service.file_object_serializer import FileObjectSerializer
-from executor.app.service.persistence_service import PersistenceService
-from executor.app.service.supervisor_service import SupervisorService
-
-
-def register_dependencies() -> None:
-    inject.configure(_configure)
-
-
-def _configure(binder: inject.Binder) -> None:
-    app_config = AppConfig()
-    file_handler_service = FileHandlerService(app_config)
-    serializer = FileObjectSerializer(file_handler_service)
-    server_proxy = ServerProxy(app_config.SERVER_URL)
-    binder.bind(DataLoader, DataLoader(app_config))
-    binder.bind(PersistenceService, PersistenceService(serializer))
-    binder.bind(SupervisorService, SupervisorService(server_proxy))
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/exception/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/exception/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception.py b/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception.py
deleted file mode 100644
index a2e8e9b..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-class UnsupportedProtocolException(Exception):
-    pass
-
-
-class FilesNotFoundException(Exception):
-    pass
-
-
-class DataLoaderNotFoundException(Exception):
-    pass
-
-
-class DataLoaderAlreadyRunningException(Exception):
-    pass
-
-
-class DataLoaderTaskNotFoundException(Exception):
-    pass
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception_handler.py b/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception_handler.py
deleted file mode 100644
index f886a12..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/exception/exception_handler.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-
-from fastapi import Request, status
-from fastapi.responses import JSONResponse
-
-logger = logging.getLogger(__name__)
-
-
-async def handle_bad_request(_: Request, exception: Exception) -> JSONResponse:
-    logger.exception("Bad request")
-    return JSONResponse(
-        status_code=status.HTTP_400_BAD_REQUEST,
-        content={
-            "exception": str(exception.__class__.__name__),
-            "detail": str(exception),
-        },
-    )
-
-
-async def handle_generic_exception(_: Request, exception: Exception) -> JSONResponse:
-    logger.exception("Unexpected exception happened")
-    return JSONResponse(
-        status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-        content={
-            "exception": str(exception.__class__.__name__),
-            "detail": str(exception),
-        },
-    )
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/main.py b/src/bonus_superlinked_rag/server/runner/executor/app/main.py
deleted file mode 100644
index 124a98d..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/main.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-from json import JSONDecodeError
-
-import uvicorn
-from fastapi import FastAPI
-from fastapi_restful.timing import add_timing_middleware
-from superlinked.framework.common.parser.exception import MissingIdException
-from superlinked.framework.online.dag.exception import ValueNotProvidedException
-
-from executor.app.configuration.app_config import AppConfig
-from executor.app.dependency_register import register_dependencies
-from executor.app.exception.exception_handler import (
-    handle_bad_request,
-    handle_generic_exception,
-)
-from executor.app.middleware.lifespan_event import lifespan
-from executor.app.router.management_router import router as management_router
-
-app_config = AppConfig()
-
-logging.basicConfig(level=app_config.LOG_LEVEL)
-logger = logging.getLogger(__name__)
-logging.getLogger("sentence_transformers").setLevel(logging.WARNING)
-
-app = FastAPI(lifespan=lifespan)
-
-app.add_exception_handler(ValueNotProvidedException, handle_bad_request)
-app.add_exception_handler(MissingIdException, handle_bad_request)
-app.add_exception_handler(JSONDecodeError, handle_bad_request)
-app.add_exception_handler(Exception, handle_generic_exception)
-
-app.include_router(management_router)
-
-add_timing_middleware(app, record=logger.info)
-
-register_dependencies()
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8080)  # noqa: S104 hardcoded-bind-all-interfaces
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/middleware/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/middleware/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/middleware/lifespan_event.py b/src/bonus_superlinked_rag/server/runner/executor/app/middleware/lifespan_event.py
deleted file mode 100644
index a6fc839..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/middleware/lifespan_event.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Temporary ignore due to vdb connector access. Fix in: FAI-1961
-# ruff: noqa: SLF001
-
-import logging
-from collections.abc import AsyncGenerator, Sequence
-from contextlib import asynccontextmanager
-
-import inject
-from fastapi import FastAPI
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.space.recency_space import RecencySpace
-from superlinked.framework.storage.in_memory.in_memory_vdb import InMemoryVDB
-
-from executor.app.configuration.app_config import AppConfig
-from executor.app.service.data_loader import DataLoader
-from executor.app.service.persistence_service import PersistenceService
-from executor.app.util.fast_api_handler import FastApiHandler
-from executor.app.util.registry_loader import RegistryLoader
-
-logger = logging.getLogger(__name__)
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
-    """
-    Handle the lifecycle of the application.
-    All the setup should be done before the yield, then the teardown happens after the yield.
-    Only graceful shutdown will let the teardown happen.
-    """
-    setup_application(app)
-    yield
-    teardown_application(app)
-
-
-def setup_application(app: FastAPI) -> None:
-    persistence_service = inject.instance(PersistenceService)
-    data_loader = inject.instance(DataLoader)
-    app_config = inject.instance(AppConfig)
-
-    if registry := RegistryLoader.get_registry(app_config.APP_MODULE_PATH):
-        for executor in registry.get_executors():
-            if isinstance(executor, RestExecutor):
-                logger.info("Found an executor, registering the endpoints")
-                if has_recency_space(executor._indices) and app_config.DISABLE_RECENCY_SPACE:
-                    msg = (
-                        "RecencySpace found in Index but is disabled. "
-                        "Either enable RecencySpace or remove it from the Index."
-                    )
-                    raise ValueError(msg)
-                try:
-                    rest_app = executor.run()
-                except Exception:  # pylint: disable=broad-exception-caught
-                    msg = "An error occurred during app execution."
-                    logger.exception(msg)
-                    return
-                if rest_app.data_loader_sources:
-                    data_loader.register_data_loader_sources(rest_app.data_loader_sources)
-                if isinstance(rest_app.online_app.storage_manager._vdb_connector, InMemoryVDB):
-                    persistence_service.register(rest_app)
-                fast_api_handler = FastApiHandler(rest_app.handler)
-
-                for path in rest_app.handler.ingest_paths:
-                    app.add_api_route(path=path, endpoint=fast_api_handler.ingest, methods=["POST"])
-
-                for path in rest_app.handler.query_paths:
-                    app.add_api_route(path=path, endpoint=fast_api_handler.query, methods=["POST"])
-    persistence_service.restore()
-
-
-def teardown_application(_: FastAPI) -> None:
-    persistence_service = inject.instance(PersistenceService)
-    logger.info("Shutdown event detected. Persisting the database")
-    persistence_service.persist()
-
-
-# Temporary recency space deactivation. Refer to: FAI-1965 for details
-def has_recency_space(indices: Sequence[Index]) -> bool:
-    return any(
-        isinstance(space, RecencySpace) for index in indices if hasattr(index, "__spaces") for space in index.__spaces
-    )
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/router/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/router/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/router/management_router.py b/src/bonus_superlinked_rag/server/runner/executor/app/router/management_router.py
deleted file mode 100644
index 7052499..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/router/management_router.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import inject
-from fastapi import APIRouter, Depends
-from fastapi.responses import JSONResponse
-from fastapi_restful.cbv import cbv
-from pydantic import BaseModel
-from starlette import status
-
-from executor.app.exception.exception import (
-    DataLoaderAlreadyRunningException,
-    DataLoaderNotFoundException,
-    DataLoaderTaskNotFoundException,
-)
-from executor.app.service.data_loader import DataLoader
-from executor.app.service.supervisor_service import SupervisorService
-
-router = APIRouter()
-
-
-class LoadDataPayload(BaseModel):
-    run_in_background: bool = False
-
-
-@cbv(router)
-class ManagementRouter:
-    __supervisor_service: SupervisorService = Depends(lambda: inject.instance(SupervisorService))
-    __data_loader: DataLoader = Depends(lambda: inject.instance(DataLoader))
-
-    @router.get(
-        "/health",
-        summary="Returns with 200 OK, if the application is healthy",
-        status_code=status.HTTP_200_OK,
-    )
-    async def health_check(self) -> JSONResponse:
-        return JSONResponse(content={"message": "OK"}, status_code=status.HTTP_200_OK)
-
-    @router.post(
-        "/reload",
-        summary="Returns with 200 and the result of the reload",
-        status_code=status.HTTP_200_OK,
-    )
-    async def reload(self) -> JSONResponse:
-        restart_result = self.__supervisor_service.restart()  # pylint: disable=no-member
-        return JSONResponse(content={"result": restart_result}, status_code=status.HTTP_200_OK)
-
-    @router.post(
-        "/data-loader/{name}/run",
-        summary=(
-            "Returns with 202 ACCEPTED if the data loader found and started in the background. "
-            "If the data loader is not found by the given name, 404 NOT FOUND."
-        ),
-        status_code=status.HTTP_200_OK,
-    )
-    async def load_data(self, name: str) -> JSONResponse:
-        try:
-            self.__data_loader.load(name)  # pylint: disable=no-member
-            return JSONResponse(
-                content={"result": f"Background task successfully started with name: {name}"},
-                status_code=status.HTTP_202_ACCEPTED,
-            )
-        except DataLoaderNotFoundException:
-            return JSONResponse(
-                content={"result": f"Data load initiation failed, no source found with name: {name}"},
-                status_code=status.HTTP_404_NOT_FOUND,
-            )
-        except DataLoaderAlreadyRunningException:
-            return JSONResponse(
-                content={"result": f"Data load already running with name: {name}"},
-                status_code=status.HTTP_409_CONFLICT,
-            )
-
-    @router.get(
-        "/data-loader/{name}/status",
-        summary=(
-            "Returns the status of a specific data loader task by its name. "
-            "200 OK with result if exists, otherwise 404 NOT FOUND."
-        ),
-        status_code=status.HTTP_200_OK,
-    )
-    async def get_data_loader_status(self, name: str) -> JSONResponse:
-        try:
-            task_status = self.__data_loader.get_task_status_by_name(name)  # pylint: disable=no-member
-            return JSONResponse(content={"result": task_status}, status_code=status.HTTP_200_OK)
-        except DataLoaderTaskNotFoundException:
-            return JSONResponse(
-                content={"result": f"Task not found with name: {name}"}, status_code=status.HTTP_404_NOT_FOUND
-            )
-
-    @router.get(
-        "/data-loader/",
-        summary="Returns 200 OK with the name and config of your data loaders",
-        status_code=status.HTTP_200_OK,
-    )
-    async def get_data_loaders(self) -> JSONResponse:
-        loaders = self.__data_loader.get_data_loaders()  # pylint: disable=no-member
-        return JSONResponse(
-            content={"result": {name: str(config) for name, config in loaders.items()}}, status_code=status.HTTP_200_OK
-        )
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/data_loader.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/data_loader.py
deleted file mode 100644
index 1297e98..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/service/data_loader.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import asyncio
-import logging
-import traceback
-from collections.abc import Sequence
-from typing import Any
-
-import pandas as pd
-from pandas.io.json._json import JsonReader
-from pandas.io.parsers import TextFileReader
-from pydantic.alias_generators import to_snake
-from superlinked.framework.dsl.source.data_loader_source import DataFormat, DataLoaderConfig, DataLoaderSource
-
-from executor.app.configuration.app_config import AppConfig
-from executor.app.exception.exception import (
-    DataLoaderAlreadyRunningException,
-    DataLoaderNotFoundException,
-    DataLoaderTaskNotFoundException,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class DataLoader:
-    def __init__(self, app_config: AppConfig) -> None:
-        self._app_config = app_config
-        self._data_loader_sources: dict[str, DataLoaderSource] = {}
-        self._data_loader_tasks: dict[str, asyncio.Task] = {}
-
-    def register_data_loader_sources(self, data_loader_sources: Sequence[DataLoaderSource]) -> None:
-        for source in data_loader_sources:
-            if source.name in self._data_loader_sources:
-                logger.warning(
-                    "Data loader source with the name '%s' is already registered. Skipping registration.", source.name
-                )
-                continue
-            self._data_loader_sources[to_snake(source.name)] = source
-
-    def get_data_loaders(self) -> dict[str, DataLoaderConfig]:
-        return {name: source.config for name, source in self._data_loader_sources.items()}
-
-    def load(self, name: str) -> None:
-        data_loader_source = self._data_loader_sources.get(name)
-        if not data_loader_source:
-            msg = f"Data loader with name: {name} not found"
-            raise DataLoaderNotFoundException(msg)
-        task = self._data_loader_tasks.get(name)
-        if task and not task.done():
-            msg = f"Data loader already running with name: {name}"
-            raise DataLoaderAlreadyRunningException(msg)
-        logger.info("Starting data load for source with the following configuration: %s", data_loader_source.config)
-        task = asyncio.create_task(asyncio.to_thread(self.__read_and_put_data, data_loader_source))
-        self._data_loader_tasks.update({name: task})
-
-    def get_task_status_by_name(self, name: str) -> str | None:
-        task = self._data_loader_tasks.get(name)
-        if task is None:
-            msg = "Data loader task not found with name: %s"
-            raise DataLoaderTaskNotFoundException(msg, name)
-
-        if not task.done():
-            return "Task is still running"
-
-        if task.exception() is not None:
-            if exc := task.exception():
-                logger.error(traceback.format_exception(type(exc), exc, exc.__traceback__))
-            return f"Task failed with exception: {task.exception()}. For traceback, check the logs."
-
-        return "Task completed successfully"
-
-    def __read_and_put_data(self, source: DataLoaderSource) -> None:
-        data = self.__read_data(source.config.path, source.config.format, source.config.pandas_read_kwargs)
-        if isinstance(data, pd.DataFrame):
-            if logger.isEnabledFor(logging.DEBUG):
-                data.info(memory_usage=True)
-            logger.debug(
-                "Data frame of size: %s has been loaded into memory. Beginning persistence process.", len(data)
-            )
-            source._source.put(data)  # noqa: SLF001 private-member-access
-        elif isinstance(data, TextFileReader | JsonReader):
-            for chunk in data:
-                if logger.isEnabledFor(logging.DEBUG):
-                    chunk.info(memory_usage=True)
-                logger.debug(
-                    "Chunk of size: %s has been loaded into memory. Beginning persistence process.", len(chunk)
-                )
-                source._source.put(chunk)  # noqa: SLF001 private-member-access
-        else:
-            error_message = (
-                "The returned object from the Pandas read method was not of the "
-                f"expected type. Actual type: {type(data)}"
-            )
-            raise TypeError(error_message)
-        logger.info("Finished the data load for source with the following configuration: %s", source.config)
-
-    def __read_data(
-        self, path: str, data_format: DataFormat, pandas_read_kwargs: dict[str, Any] | None
-    ) -> pd.DataFrame | TextFileReader | JsonReader:
-        kwargs = pandas_read_kwargs or {}
-        match data_format:
-            case DataFormat.CSV:
-                return pd.read_csv(path, **kwargs)
-            case DataFormat.FWF:
-                return pd.read_fwf(path, **kwargs)
-            case DataFormat.XML:
-                return pd.read_xml(path, **kwargs)
-            case DataFormat.JSON:
-                return pd.read_json(path, **kwargs)
-            case DataFormat.PARQUET:
-                return pd.read_parquet(path, **kwargs)
-            case DataFormat.ORC:
-                return pd.read_orc(path, **kwargs)
-            case _:
-                msg = "Unsupported data format: %s"
-                raise ValueError(msg, data_format)
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/file_handler_service.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/file_handler_service.py
deleted file mode 100644
index 07a9bff..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/service/file_handler_service.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import hashlib
-import os
-from enum import Enum
-
-from executor.app.configuration.app_config import AppConfig
-
-
-class HashType(Enum):
-    MD5 = hashlib.md5
-
-
-class FileHandlerService:
-    def __init__(self, app_config: AppConfig, hash_type: HashType | None = None) -> None:
-        self.__hash_type = hash_type or HashType.MD5
-        self.app_config = app_config
-
-    def generate_filename(self, field_id: str, app_id: str) -> str:
-        filename = self.__hash_type.value(f"{app_id}_{field_id}".encode()).hexdigest()
-        return f"{self.app_config.PERSISTENCE_FOLDER_PATH}/{filename}.json"
-
-    def ensure_folder(self) -> None:
-        os.makedirs(self.app_config.PERSISTENCE_FOLDER_PATH, exist_ok=True)
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/file_object_serializer.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/file_object_serializer.py
deleted file mode 100644
index ab6c739..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/service/file_object_serializer.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import logging
-import os
-
-from superlinked.framework.storage.in_memory.object_serializer import ObjectSerializer
-
-from executor.app.service.file_handler_service import FileHandlerService
-
-logger = logging.getLogger(__name__)
-
-
-EMPTY_JSON_OBJECT_SIZE = 4  # 4 characters: "{}"
-
-
-class FileObjectSerializer(ObjectSerializer):
-    def __init__(self, file_handler_service: FileHandlerService) -> None:
-        super().__init__()
-        self.__file_handler_service = file_handler_service
-
-    def write(self, field_identifier: str, serialized_object: str, app_identifier: str) -> None:
-        self.__file_handler_service.ensure_folder()
-
-        logger.info("Persisting database with field id: %s and app id: %s", field_identifier, app_identifier)
-        file_with_path = self.__file_handler_service.generate_filename(field_identifier, app_identifier)
-        with open(file_with_path, "w", encoding="utf-8") as file:
-            logger.debug("Writing field: %s and app: %s file to: %s", field_identifier, app_identifier, file_with_path)
-            json.dump(serialized_object, file)
-
-    def read(self, field_identifier: str, app_identifier: str) -> str:
-        logger.info("Restoring database using field id: %s and app id: %s", field_identifier, app_identifier)
-        file_with_path = self.__file_handler_service.generate_filename(field_identifier, app_identifier)
-
-        result = "{}"
-        try:
-            if os.path.isfile(file_with_path):
-                with open(file_with_path, encoding="utf-8") as file:
-                    if os.stat(file_with_path).st_size >= EMPTY_JSON_OBJECT_SIZE:
-                        result = json.load(file)
-        except json.JSONDecodeError:
-            logger.exception("File is present but contains invalid data. File: %s", file_with_path)
-        except Exception:  # pylint: disable=broad-exception-caught
-            logger.exception("An error occurred during the file read operation. File: %s", file_with_path)
-        return result
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/persistence_service.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/persistence_service.py
deleted file mode 100644
index 860f31b..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/service/persistence_service.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-
-from superlinked.framework.dsl.executor.rest.rest_executor import RestApp
-
-from executor.app.service.file_object_serializer import FileObjectSerializer
-
-logger = logging.getLogger(__name__)
-
-
-class PersistenceService:
-    def __init__(self, serializer: FileObjectSerializer) -> None:
-        self._applications: list[RestApp] = []
-        self._serializer = serializer
-
-    def register(self, rest_app: RestApp) -> None:
-        if rest_app in self._applications:
-            logger.warning("Application already exists: %s", rest_app)
-            return
-        logger.info("Rest app registered: %s", rest_app)
-        self._applications.append(rest_app)
-
-    def persist(self) -> None:
-        for app in self._applications:
-            app.online_app.persist(self._serializer)
-
-    def restore(self) -> None:
-        for app in self._applications:
-            app.online_app.restore(self._serializer)
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/service/supervisor_service.py b/src/bonus_superlinked_rag/server/runner/executor/app/service/supervisor_service.py
deleted file mode 100644
index 9645aea..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/service/supervisor_service.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from xmlrpc.client import ServerProxy
-
-
-class SupervisorService:
-    def __init__(self, server_proxy: ServerProxy) -> None:
-        self.server = server_proxy
-
-    def restart(self) -> str:
-        """Restart the API via supervisor XML-RPC and return the result."""
-        return str(self.server.supervisor.restart())
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/util/__init__.py b/src/bonus_superlinked_rag/server/runner/executor/app/util/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/util/fast_api_handler.py b/src/bonus_superlinked_rag/server/runner/executor/app/util/fast_api_handler.py
deleted file mode 100644
index edaad0f..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/util/fast_api_handler.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any
-
-from fastapi import Request, Response, status
-from fastapi.responses import JSONResponse
-from pydantic import Field
-from superlinked.framework.common.util.immutable_model import ImmutableBaseModel
-from superlinked.framework.dsl.executor.rest.rest_handler import RestHandler
-
-
-class QueryResponse(ImmutableBaseModel):
-    schema_: str = Field(..., alias="schema")
-    results: list[dict[str, Any]]
-
-
-class FastApiHandler:
-    def __init__(self, rest_handler: RestHandler) -> None:
-        self.__rest_handler = rest_handler
-
-    async def ingest(self, request: Request) -> Response:
-        payload = await request.json()
-        self.__rest_handler._ingest_handler(payload, request.url.path)  # noqa: SLF001 private-member-access
-        return Response(status_code=status.HTTP_202_ACCEPTED)
-
-    async def query(self, request: Request) -> Response:
-        payload = await request.json()
-        result = self.__rest_handler._query_handler(payload, request.url.path)  # noqa: SLF001 private-member-access
-        query_response = QueryResponse(
-            schema=result.schema._schema_name,  # noqa: SLF001 private-member-access
-            results=[
-                {
-                    "entity": {
-                        "id": entry.entity.header.object_id,
-                        "origin": (
-                            {
-                                "id": entry.entity.header.object_id,
-                                "schema": entry.entity.header.schema_id,
-                            }
-                            if entry.entity.header.origin_id
-                            else {}
-                        ),
-                    },
-                    "obj": entry.stored_object,
-                }
-                for entry in result.entries
-            ],
-        )
-        return JSONResponse(
-            content=query_response.model_dump(by_alias=True),
-            status_code=status.HTTP_200_OK,
-        )
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/util/open_api_description_util.py b/src/bonus_superlinked_rag/server/runner/executor/app/util/open_api_description_util.py
deleted file mode 100644
index b6b49cd..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/util/open_api_description_util.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import json
-import logging
-import os
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-
-class OpenApiDescriptionUtil:
-    @staticmethod
-    def get_open_api_description_by_key(key: str, file_path: str | None = None) -> dict[str, Any]:
-        if file_path is None:
-            file_path = os.path.join(os.getcwd(), "executor/openapi/static_endpoint_descriptor.json")
-        with open(file_path, encoding="utf-8") as file:
-            data = json.load(file)
-            open_api_description = data.get(key)
-            if open_api_description is None:
-                logger.warning("No OpenAPI description found for key: %s", key)
-            return open_api_description
diff --git a/src/bonus_superlinked_rag/server/runner/executor/app/util/registry_loader.py b/src/bonus_superlinked_rag/server/runner/executor/app/util/registry_loader.py
deleted file mode 100644
index 6dcfc43..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/app/util/registry_loader.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-from importlib import import_module
-
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-
-logger = logging.getLogger(__name__)
-
-
-class RegistryLoader:
-    @staticmethod
-    def get_registry(app_module_path: str) -> SuperlinkedRegistry | None:
-        try:
-            return import_module(app_module_path).SuperlinkedRegistry
-        except ImportError:
-            logger.exception("Module not found at: %s", app_module_path)
-        except AttributeError:
-            logger.exception("SuperlinkedRegistry not found in module: %s", app_module_path)
-        except Exception:  # pylint: disable=broad-exception-caught
-            logger.exception("An unexpected error occurred while loading the module at: %s", app_module_path)
-        return None
diff --git a/src/bonus_superlinked_rag/server/runner/executor/openapi/static_endpoint_descriptor.json b/src/bonus_superlinked_rag/server/runner/executor/openapi/static_endpoint_descriptor.json
deleted file mode 100644
index fb184be..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/openapi/static_endpoint_descriptor.json
+++ /dev/null
@@ -1,144 +0,0 @@
-{
-    "ingest": {
-        "requestBody": {
-            "description": "Ensure the request body includes all fields defined by your schema. Note that only the ID is shown here for simplicity.",
-            "content": {
-                "application/json": {
-                    "schema": {
-                        "required": [
-                            "id"
-                        ],
-                        "type": "object",
-                        "properties": {
-                            "id": {
-                                "type": "string"
-                            }
-                        }
-                    }
-                }
-            },
-            "required": true
-        },
-        "responses": {
-            "202": {
-                "description": "Accepted"
-            },
-            "400": {
-                "description": "Bad Request",
-                "content": {
-                    "application/json": {
-                        "schema": {
-                            "type": "object",
-                            "properties": {
-                                "exception": {
-                                    "type": "string",
-                                    "example": "ValueNotProvidedException"
-                                },
-                                "detail": {
-                                    "type": "string",
-                                    "example": "(\"The SchemaField CarSchema.make doesn't have a default value and was not provided in the ParsedSchema.\",)"
-                                }
-                            },
-                            "required": [
-                                "exception",
-                                "detail"
-                            ]
-                        }
-                    }
-                }
-            }
-        }
-    },
-    "query": {
-        "requestBody": {
-            "description": "The request body must include all mandatory `Param` objects specified in the query that you set. If any are missing, an error indicating the missing parameter will be returned.",
-            "content": {
-                "application/json": {
-                    "schema": {
-                        "type": "object"
-                    }
-                }
-            },
-            "required": true
-        },
-        "responses": {
-            "200": {
-                "description": "Successful Response",
-                "content": {
-                    "application/json": {
-                        "schema": {
-                            "type": "object",
-                            "properties": {
-                                "schema": {
-                                    "type": "string"
-                                },
-                                "results": {
-                                    "type": "array",
-                                    "items": {
-                                        "type": "object",
-                                        "properties": {
-                                            "entity": {
-                                                "type": "object",
-                                                "properties": {
-                                                    "id": {
-                                                        "type": "string"
-                                                    },
-                                                    "origin": {
-                                                        "type": "object",
-                                                        "properties": {
-                                                            "id": {
-                                                                "type": "string"
-                                                            },
-                                                            "schema": {
-                                                                "type": "string"
-                                                            }
-                                                        },
-                                                        "additionalProperties": false
-                                                    }
-                                                },
-                                                "additionalProperties": false
-                                            },
-                                            "obj": {
-                                                "type": "object",
-                                                "additionalProperties": true
-                                            }
-                                        },
-                                        "additionalProperties": false
-                                    }
-                                }
-                            },
-                            "required": [
-                                "schema",
-                                "results"
-                            ]
-                        }
-                    }
-                }
-            },
-            "500": {
-                "description": "Internal Server Error",
-                "content": {
-                    "application/json": {
-                        "schema": {
-                            "type": "object",
-                            "properties": {
-                                "exception": {
-                                    "type": "string",
-                                    "example": "QueryException"
-                                },
-                                "detail": {
-                                    "type": "string",
-                                    "example": "Though parameter 'limit' was defined in the query, its value was not provided. Set it properly or register a new query."
-                                }
-                            },
-                            "required": [
-                                "exception",
-                                "detail"
-                            ]
-                        }
-                    }
-                }
-            }
-        }
-    }
-}
diff --git a/src/bonus_superlinked_rag/server/runner/executor/supervisord.conf b/src/bonus_superlinked_rag/server/runner/executor/supervisord.conf
deleted file mode 100644
index 4ac626c..0000000
--- a/src/bonus_superlinked_rag/server/runner/executor/supervisord.conf
+++ /dev/null
@@ -1,18 +0,0 @@
-[supervisord]
-
-[inet_http_server]
-port=127.0.0.1:9001
-
-[rpcinterface:supervisor]
-supervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface
-
-[program:uvicorn_host]
-command=/opt/pysetup/.venv/bin/python -m uvicorn executor.app.main:app --host 0.0.0.0 --port 8080
-numprocs=1
-process_name=uvicorn_host
-stdout_logfile=/dev/stdout
-stdout_logfile_maxbytes=0
-stderr_logfile=/dev/stderr
-stderr_logfile_maxbytes=0
-autorestart=true
-
diff --git a/src/bonus_superlinked_rag/server/runner/poetry.lock b/src/bonus_superlinked_rag/server/runner/poetry.lock
deleted file mode 100644
index e6a41a2..0000000
--- a/src/bonus_superlinked_rag/server/runner/poetry.lock
+++ /dev/null
@@ -1,5198 +0,0 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
-
-[[package]]
-name = "adlfs"
-version = "2023.12.0"
-description = "Access Azure Datalake Gen1 with fsspec and dask"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "adlfs-2023.12.0-py3-none-any.whl", hash = "sha256:da6e391afd002c7bb1b75dcc286b78fdf5dbf29aca0f984462033df6311b4e7e"},
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-]
-
-[package.dependencies]
-aiohttp = ">=3.7.0"
-azure-core = ">=1.23.1,<2.0.0"
-azure-datalake-store = ">=0.0.46,<0.1"
-azure-identity = "*"
-azure-storage-blob = ">=12.12.0"
-fsspec = ">=2023.12.0"
-
-[package.extras]
-docs = ["furo", "myst-parser", "numpydoc", "sphinx"]
-tests = ["arrow", "dask[dataframe]", "docker", "pytest", "pytest-mock"]
-
-[[package]]
-name = "aiobotocore"
-version = "2.13.1"
-description = "Async client for aws services using botocore and aiohttp"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "aiobotocore-2.13.1-py3-none-any.whl", hash = "sha256:1bef121b99841ee3cc788e4ed97c332ba32353b1f00e886d1beb3aae95520858"},
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-]
-
-[package.dependencies]
-aiohttp = ">=3.9.2,<4.0.0"
-aioitertools = ">=0.5.1,<1.0.0"
-botocore = ">=1.34.70,<1.34.132"
-wrapt = ">=1.10.10,<2.0.0"
-
-[package.extras]
-awscli = ["awscli (>=1.32.70,<1.33.14)"]
-boto3 = ["boto3 (>=1.34.70,<1.34.132)"]
-
-[[package]]
-name = "aiohttp"
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-description = "Async http client/server framework (asyncio)"
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-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.10.3,<=3.12.3"
-content-hash = "b460406639e6feab0fc35f96ffd4fa8e4b376cb55e1ce5f82f4ea72baabc330c"
diff --git a/src/bonus_superlinked_rag/server/runner/poller/Dockerfile b/src/bonus_superlinked_rag/server/runner/poller/Dockerfile
deleted file mode 100644
index 06ca086..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/Dockerfile
+++ /dev/null
@@ -1,51 +0,0 @@
-# --- Python base ---
-
-FROM python:3.11.9 AS python-base
-
-ENV PYTHONUNBUFFERED=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    \
-    PIP_NO_CACHE_DIR=off \
-    PIP_DISABLE_PIP_VERSION_CHECK=on \
-    PIP_DEFAULT_TIMEOUT=100 \
-    \
-    POETRY_VERSION=1.8.2 \
-    POETRY_HOME="/opt/poetry" \
-    POETRY_VIRTUALENVS_IN_PROJECT=true \
-    POETRY_NO_INTERACTION=1 \
-    \
-    PYSETUP_PATH="/opt/pysetup" \
-    VENV_PATH="/opt/pysetup/.venv"
-
-ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
-
-# --- Builder base ---
-
-FROM python-base AS builder-base
-RUN apt-get update \
-    && apt-get install --no-install-recommends -y \
-    curl \
-    build-essential \
-    && apt-get clean && rm -rf /var/lib/apt/lists/*
-
-SHELL ["/bin/bash", "-o", "pipefail", "-c"]
-RUN curl -sSL https://install.python-poetry.org | python -
-
-WORKDIR $PYSETUP_PATH
-
-COPY poetry.lock pyproject.toml $PYSETUP_PATH/
-RUN poetry install --no-root --only poller
-
-# --- Poller ---
-
-FROM python:3.11.5-alpine AS poller
-
-COPY --from=builder-base /opt/pysetup/.venv /opt/pysetup/.venv
-
-WORKDIR /code
-
-#COPY config /code/config
-COPY poller/app /code/poller/app
-COPY poller/logging_config.ini poller/poller_config.ini /code/poller/
-
-ENTRYPOINT ["/opt/pysetup/.venv/bin/python", "-m", "poller.app.main", "--config_path", "/code/config/config.yaml"]
diff --git a/src/bonus_superlinked_rag/server/runner/poller/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/app_location_parser/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/app_location_parser/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/app_location_parser/app_location_parser.py b/src/bonus_superlinked_rag/server/runner/poller/app/app_location_parser/app_location_parser.py
deleted file mode 100644
index e6a7885..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/app_location_parser/app_location_parser.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import re
-from dataclasses import dataclass
-from enum import Enum
-
-from poller.app.config.poller_config import PollerConfig
-
-
-class StorageType(Enum):
-    S3 = "s3"
-    GCS = "gcs"
-    LOCAL = "local"
-
-
-@dataclass
-class AppLocation:
-    type_: StorageType
-    bucket: str | None
-    path: str
-
-
-class AppLocationParser:
-    def __init__(self) -> None:
-        self.config = PollerConfig()
-        self.logger = self.config.setup_logger(__name__)
-
-    def _get_bucket_and_path_or_raise(self, pattern: str, app_location: str) -> tuple[str, str]:
-        match = re.search(pattern, app_location)
-        if not match:
-            msg = f"Invalid location format {app_location}"
-            raise ValueError(msg)
-        try:
-            bucket, path = match.groups()
-        except ValueError as e:
-            msg = f"Expected bucket and path in {app_location}"
-            raise ValueError(msg) from e
-        return bucket, path
-
-    def parse(self, app_location: str) -> AppLocation:
-        """
-        Parse the application location URL to extract the storage type, bucket, and path.
-        """
-        self.logger.info("App location received: %s", app_location)
-        bucket: str | None
-        pattern: str
-
-        match app_location:
-            case app_location if app_location.startswith(("s3://", "s3a://", "s3n://")):
-                type_ = StorageType.S3
-                pattern = r"s3[a|n]?://([^/]+)/(.+)"
-                bucket, path = self._get_bucket_and_path_or_raise(pattern, app_location)
-            case app_location if "amazonaws.com" in app_location:
-                type_ = StorageType.S3
-                pattern = r"https?://(?:s3)[^/]*.amazonaws.com/([^/]+)/(.+)"
-                bucket, path = self._get_bucket_and_path_or_raise(pattern, app_location)
-            case app_location if "storage.googleapis.com" in app_location or "storage.cloud.google.com" in app_location:
-                type_ = StorageType.GCS
-                pattern = r"https?://(?:storage\.cloud\.google\.com|storage\.googleapis\.com)/([^/]+)/(.+)"
-                bucket, path = self._get_bucket_and_path_or_raise(pattern, app_location)
-            case app_location if app_location.startswith("gs://"):
-                type_ = StorageType.GCS
-                pattern = r"gs://([^/]+)/(.+)"
-                bucket, path = self._get_bucket_and_path_or_raise(pattern, app_location)
-            case app_location if app_location == "local":
-                type_ = StorageType.LOCAL
-                bucket = None
-                path = "/src/app.py"
-            case app_location if app_location.startswith("/"):
-                type_ = StorageType.LOCAL
-                bucket = None
-                path = app_location
-            case _:
-                msg = "Unsupported storage location"
-                raise ValueError(msg)
-        return AppLocation(type_=type_, bucket=bucket, path=path)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/config/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/config/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/config/poller_config.py b/src/bonus_superlinked_rag/server/runner/poller/app/config/poller_config.py
deleted file mode 100644
index 8617614..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/config/poller_config.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import configparser
-import logging
-import logging.config
-
-
-class PollerConfig:
-    def __init__(self) -> None:
-        poller_dir = "poller"
-        poller_config_path = f"{poller_dir}/poller_config.ini"
-        logging_config_path = f"{poller_dir}/logging_config.ini"
-
-        config = configparser.ConfigParser()
-        config.read(poller_config_path)
-
-        self.poll_interval_seconds = config.getint("POLLER", "POLL_INTERVAL_SECONDS")
-        self.executor_port = config.getint("POLLER", "EXECUTOR_PORT")
-        self.executor_url = config.get("POLLER", "EXECUTOR_URL")
-        self.aws_credentials = config.get("POLLER", "AWS_CREDENTIALS")
-        self.gcp_credentials = config.get("POLLER", "GCP_CREDENTIALS")
-        self.download_location = config.get("POLLER", "DOWNLOAD_LOCATION")
-        self.logging_config = logging_config_path
-
-    def setup_logger(self, name: str) -> logging.Logger:
-        logging.config.fileConfig(self.logging_config)
-        return logging.getLogger(name)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/main.py b/src/bonus_superlinked_rag/server/runner/poller/app/main.py
deleted file mode 100644
index 0b00ccc..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/main.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Module for polling storage services and downloading updated files."""
-
-import argparse
-
-from poller.app.poller.poller import Poller
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Start the poller with a given config file.")
-    parser.add_argument(
-        "--config_path",
-        help="Path to the configuration file.",
-        default="../config/config.yaml",
-    )
-    args = parser.parse_args()
-
-    poller = Poller(args.config_path)
-    poller.run()
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/poller/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/poller/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/poller/poller.py b/src/bonus_superlinked_rag/server/runner/poller/app/poller/poller.py
deleted file mode 100644
index 0520d24..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/poller/poller.py
+++ /dev/null
@@ -1,57 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import time
-from threading import Thread
-
-import yaml
-
-from poller.app.app_location_parser.app_location_parser import (
-    AppLocation,
-    AppLocationParser,
-)
-from poller.app.config.poller_config import PollerConfig
-from poller.app.resource_handler.resource_handler_factory import ResourceHandlerFactory
-
-
-class Poller(Thread):
-    """
-    The Poller class is a thread that polls files from different types of storage: local, S3,
-    and Google Cloud Storage at a regular interval.
-    """
-
-    def __init__(self, config_path: str) -> None:
-        Thread.__init__(self)
-        self.poller_config = PollerConfig()
-        self.app_location_config_path = config_path
-        self.app_location_config = self.parse_app_location_config()
-
-    def parse_app_location_config(self) -> AppLocation:
-        """
-        Parse the configuration from the YAML file.
-        """
-        with open(self.app_location_config_path, encoding="utf-8") as file:
-            config_yaml = yaml.safe_load(file)
-        app_location = config_yaml["app_location"]
-        return AppLocationParser().parse(app_location)
-
-    def run(self) -> None:
-        """
-        Start the polling process.
-        """
-        resource_handler = ResourceHandlerFactory.get_resource_handler(self.app_location_config)
-        while True:
-            if resource_handler.check_api_health():
-                resource_handler.poll()
-            time.sleep(self.poller_config.poll_interval_seconds)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/gcs/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/gcs/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/gcs/gcs_resource_handler.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/gcs/gcs_resource_handler.py
deleted file mode 100644
index 450ce31..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/gcs/gcs_resource_handler.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from google.auth.exceptions import DefaultCredentialsError
-from google.cloud import storage  # type: ignore[attr-defined]
-from google.cloud.exceptions import GoogleCloudError
-from google.cloud.storage.client import Client as GCSClient
-
-from poller.app.app_location_parser.app_location_parser import AppLocation
-from poller.app.resource_handler.resource_handler import ResourceHandler
-
-
-class GCSResourceHandler(ResourceHandler):
-    def __init__(self, app_location: AppLocation, client: GCSClient | None = None) -> None:
-        super().__init__(app_location)
-        self.client = client or self.initialize_gcs_client()
-
-    def initialize_gcs_client(self) -> GCSClient:
-        """
-        Initialize the GCS client, with fallback to credentials file if necessary.
-        """
-        try:
-            # First, try to create a GCS client without explicit credentials
-            client = storage.Client()
-            client.get_bucket(self.app_location.bucket)  # Test access
-        except (GoogleCloudError, DefaultCredentialsError):
-            # If the first method fails, try to use credentials from a JSON file
-            try:
-                return storage.Client.from_service_account_json(
-                    json_credentials_path=self.poller_config.gcp_credentials,
-                )
-            except FileNotFoundError as e:
-                msg = "Could not find GCP credentials file and no service account available."
-                raise FileNotFoundError(msg) from e
-        else:
-            return client
-
-    def poll(self) -> None:
-        """
-        Poll files from a Google Cloud Storage bucket and download new or modified files.
-        """
-        bucket = self.client.get_bucket(self.get_bucket())
-        blobs = bucket.list_blobs(prefix=self.app_location.path)
-        for blob in blobs:
-            self.check_and_download(blob.updated, blob.name)
-
-    def download_file(self, bucket_name: str | None, object_name: str, download_path: str) -> None:
-        """
-        Download a file from GCS to the specified path.
-        """
-        bucket = self.client.get_bucket(bucket_name)
-        blob = bucket.blob(object_name)
-        blob.download_to_filename(download_path)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/local/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/local/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/local/local_resource_handler.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/local/local_resource_handler.py
deleted file mode 100644
index bb640d2..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/local/local_resource_handler.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import shutil
-from datetime import datetime, timezone
-
-from poller.app.resource_handler.resource_handler import ResourceHandler
-
-
-class LocalResourceHandler(ResourceHandler):
-    def get_bucket(self) -> str:
-        return "local"
-
-    def download_file(self, _: str | None, object_name: str, download_path: str) -> None:
-        """
-        'Download' a file from local storage to the specified path.
-        In this case, it's just copying the file.
-        """
-        self.logger.info("Copy file from %s to %s", object_name, download_path)
-        shutil.copy2(object_name, download_path)
-
-    def poll(self) -> None:
-        """
-        Poll files from a local directory and notify about new or modified files.
-        """
-        self.logger.info("Polling files from: %s", self.app_location.path)
-        if not self._path_exists():
-            self.logger.error("Path does not exist: %s", self.app_location.path)
-            return
-        self._process_path()
-
-    def _path_exists(self) -> bool:
-        return os.path.exists(self.app_location.path)
-
-    def _process_path(self) -> None:
-        if os.path.isfile(self.app_location.path):
-            self._process_file(self.app_location.path)
-        else:
-            self._process_directory()
-
-    def _process_directory(self) -> None:
-        for root, _, files in os.walk(self.app_location.path):
-            for file in files:
-                file_path = os.path.join(root, file)
-                self._process_file(file_path)
-
-    def _process_file(self, file_path: str) -> None:
-        self.logger.info("Found file: %s", file_path)
-        file_time = datetime.fromtimestamp(os.path.getmtime(file_path), tz=timezone.utc)
-        try:
-            self.check_and_download(file_time, self.app_location.path)
-        except (FileNotFoundError, PermissionError):
-            self.logger.exception("Failed to download and notify new version")
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler.py
deleted file mode 100644
index 31ede18..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-from abc import ABC, abstractmethod
-from datetime import datetime, timezone
-
-import requests
-from botocore.exceptions import ClientError
-from google.cloud.exceptions import GoogleCloudError
-
-from poller.app.app_location_parser.app_location_parser import AppLocation
-from poller.app.config.poller_config import PollerConfig
-
-
-class ResourceHandler(ABC):
-    def __init__(self, app_location: AppLocation) -> None:
-        self.app_location = app_location
-        self.start_time = datetime.now(tz=timezone.utc)
-        self.first_run = True
-        self.poller_config = PollerConfig()
-        self.logger = self.poller_config.setup_logger(__name__)
-
-    @abstractmethod
-    def poll(self) -> None:
-        pass
-
-    @abstractmethod
-    def download_file(self, bucket_name: str | None, object_name: str, download_path: str) -> None:
-        pass
-
-    def get_bucket(self) -> str:
-        if self.app_location.bucket is None:
-            msg = "Bucket name is None"
-            raise ValueError(msg)
-        return self.app_location.bucket
-
-    def convert_to_utc(self, dt: datetime) -> datetime:
-        """
-        Convert a datetime object to UTC timezone.
-        """
-        if dt.tzinfo is None:
-            dt = dt.replace(tzinfo=timezone.utc)
-        return dt.astimezone(timezone.utc)
-
-    def check_and_download(self, object_time: datetime, object_name: str) -> None:
-        """
-        Check if the object's time is more recent than the poller's start time and download it if so.
-        If it's the first run, download the object regardless of its time.
-        After downloading, call the executor API's /reload endpoint with a PUT request.
-        """
-        self.start_time = self.convert_to_utc(self.start_time)
-        object_time = self.convert_to_utc(object_time)
-
-        if self.first_run or object_time > self.start_time:
-            filename = os.path.basename(object_name.lstrip("/"))
-            download_path = os.path.join(self.poller_config.download_location, filename)
-            os.makedirs(os.path.dirname(download_path), exist_ok=True)
-            try:
-                self.download_file(self.get_bucket(), object_name, download_path)
-                self.logger.info("Downloaded %s to %s", object_name, download_path)
-                self.notify_executor(object_name)
-                self.start_time = datetime.now(tz=timezone.utc)
-            except (OSError, ClientError, GoogleCloudError):
-                self.logger.exception("Failed to download %s", object_name)
-
-        if self.first_run:
-            self.first_run = False
-            self.start_time = datetime.now(tz=timezone.utc)
-
-    def notify_executor(self, object_name: str) -> None:
-        """
-        Notify the executor API about the new version by making a PUT request to /reload.
-        """
-        api_url = f"{self.poller_config.executor_url}:{self.poller_config.executor_port}/reload"
-        data = {"object_name": object_name}
-        response = None
-        try:
-            response = requests.post(api_url, json=data, timeout=10)
-            response.raise_for_status()
-            self.logger.info(
-                "Successfully notified executor about new version of %s",
-                object_name,
-            )
-        except requests.HTTPError:
-            self.logger.exception(
-                "Failed to notify executor about new version of %s. Status code: %s",
-                object_name,
-                response.status_code if response else "No response received",
-            )
-        except requests.RequestException:
-            self.logger.exception(
-                "Failed to notify executor about new version of %s",
-                object_name,
-            )
-
-    def check_api_health(self) -> bool:
-        """
-        Check the health of the API and return True if it's healthy, False otherwise.
-        """
-        api_endpoint = f"{self.poller_config.executor_url}:{self.poller_config.executor_port}/health"
-        try:
-            response = requests.get(api_endpoint, timeout=10)
-            response.raise_for_status()
-        except (requests.HTTPError, requests.RequestException) as e:
-            self.logger.warning("API is not healthy! Error: %s", e)
-            return False
-        return True
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler_factory.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler_factory.py
deleted file mode 100644
index 17390df..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/resource_handler_factory.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from poller.app.app_location_parser.app_location_parser import AppLocation, StorageType
-from poller.app.resource_handler.gcs.gcs_resource_handler import GCSResourceHandler
-from poller.app.resource_handler.local.local_resource_handler import (
-    LocalResourceHandler,
-)
-from poller.app.resource_handler.resource_handler import ResourceHandler
-from poller.app.resource_handler.s3.s3_resource_handler import S3ResourceHandler
-
-
-class ResourceHandlerFactory:
-    @staticmethod
-    def get_resource_handler(app_location: AppLocation) -> ResourceHandler:
-        match app_location.type_:
-            case StorageType.S3:
-                return S3ResourceHandler(app_location)
-            case StorageType.GCS:
-                return GCSResourceHandler(app_location)
-            case StorageType.LOCAL:
-                return LocalResourceHandler(app_location)
-            case _:
-                msg = f"Invalid resource type in config: {app_location.type_}"
-                raise ValueError(msg)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/s3/__init__.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/s3/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/s3/s3_resource_handler.py b/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/s3/s3_resource_handler.py
deleted file mode 100644
index 9e81488..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/app/resource_handler/s3/s3_resource_handler.py
+++ /dev/null
@@ -1,75 +0,0 @@
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-
-import boto3
-from botocore.client import Config
-from botocore.exceptions import ClientError
-from mypy_boto3_s3.client import S3Client
-
-from poller.app.app_location_parser.app_location_parser import AppLocation
-from poller.app.resource_handler.resource_handler import ResourceHandler
-
-
-class S3ResourceHandler(ResourceHandler):
-    def __init__(
-        self,
-        app_location: AppLocation,
-        client: S3Client | None = None,
-    ) -> None:  # client=None for easier testability
-        super().__init__(app_location)
-        self.client = client or self.initialize_s3_client()
-        self.resource = boto3.resource("s3")
-
-    def initialize_s3_client(self) -> S3Client:
-        """
-        Initialize the S3 client, with fallback to credentials file if necessary.
-        """
-        try:
-            # First, try to create an S3 resource without explicit credentials
-            client = boto3.client("s3", config=Config(signature_version="s3v4"))
-            if self.app_location.bucket is not None:
-                client.head_bucket(Bucket=self.app_location.bucket)  # Test access
-        except ClientError:
-            # If the first method fails, try to use credentials from a JSON file
-            try:
-                with open(self.poller_config.aws_credentials, encoding="utf-8") as aws_cred_file:
-                    aws_credentials = json.load(aws_cred_file)
-                return boto3.client(
-                    "s3",
-                    aws_access_key_id=aws_credentials["aws_access_key_id"],
-                    aws_secret_access_key=aws_credentials["aws_secret_access_key"],
-                    region_name=aws_credentials["region"],
-                )
-            except FileNotFoundError as e:
-                msg = "Could not find AWS credentials file and no IAM role available."
-                raise FileNotFoundError(msg) from e
-        else:
-            return client
-
-    def poll(self) -> None:
-        """
-        Poll files from an S3 bucket and download new or modified files.
-        """
-        bucket = self.resource.Bucket(self.get_bucket())
-        for obj in bucket.objects.filter(Prefix=self.app_location.path):
-            self.check_and_download(obj.last_modified, obj.key)
-
-    def download_file(self, _: str | None, object_name: str, download_path: str) -> None:
-        """
-        Download a file from S3 to the specified path.
-        """
-        bucket = self.get_bucket()
-        self.client.download_file(Bucket=bucket, Key=object_name, Filename=download_path)
diff --git a/src/bonus_superlinked_rag/server/runner/poller/logging_config.ini b/src/bonus_superlinked_rag/server/runner/poller/logging_config.ini
deleted file mode 100644
index 2d28d78..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/logging_config.ini
+++ /dev/null
@@ -1,28 +0,0 @@
-[loggers]
-keys=root,sampleLogger
-
-[handlers]
-keys=consoleHandler
-
-[formatters]
-keys=sampleFormatter
-
-[logger_root]
-level=INFO
-handlers=consoleHandler
-
-[logger_sampleLogger]
-level=INFO
-handlers=consoleHandler
-qualname=sampleLogger
-propagate=0
-
-[handler_consoleHandler]
-class=StreamHandler
-level=INFO
-formatter=sampleFormatter
-args=(sys.__stdout__,)
-
-[formatter_sampleFormatter]
-format=%(asctime)s - %(levelname)s - %(message)s
-datefmt=
diff --git a/src/bonus_superlinked_rag/server/runner/poller/poller_config.ini b/src/bonus_superlinked_rag/server/runner/poller/poller_config.ini
deleted file mode 100644
index e97be68..0000000
--- a/src/bonus_superlinked_rag/server/runner/poller/poller_config.ini
+++ /dev/null
@@ -1,7 +0,0 @@
-[POLLER]
-POLL_INTERVAL_SECONDS=10
-EXECUTOR_PORT=8080
-EXECUTOR_URL=http://executor
-DOWNLOAD_LOCATION=/code/data/src
-AWS_CREDENTIALS=/code/aws_credentials.json
-GCP_CREDENTIALS=/code/gcp_credentials.json
diff --git a/src/bonus_superlinked_rag/server/runner/pyproject.toml b/src/bonus_superlinked_rag/server/runner/pyproject.toml
deleted file mode 100644
index bec2f0b..0000000
--- a/src/bonus_superlinked_rag/server/runner/pyproject.toml
+++ /dev/null
@@ -1,152 +0,0 @@
-[tool.poetry]
-name = "deployment"
-version = "0.2.0"
-description = "The Superlinked single node"
-authors = ["Superlinked Release <release@superlinked.com>"]
-license = "Proprietary"
-readme = "README.md"
-
-[tool.poetry.dependencies]
-python = ">=3.10.3,<=3.12.3"
-pandas = "^2.0.3"
-
-[tool.poetry.group.poller]
-optional = true
-
-[tool.poetry.group.poller.dependencies]
-boto3 = "^1.34.13"
-google-auth = "^2.26.1"
-google-cloud-storage = "^2.14.0"
-requests = "^2.27.1"
-cerberus = "^1.3.5"
-pyyaml = "^6.0.1"
-chardet = "^5.2.0"
-boto3-stubs = {version = "^1.34.13", extras = ["s3"]}
-
-[tool.poetry.group.executor]
-optional = true
-
-[tool.poetry.group.executor.dependencies]
-fastapi = "^0.109.0"
-uvicorn = "^0.15.0"
-deltalake = "^0.15.1"
-fsspec = "^2023.12.2"
-gcsfs = "^2023.12.2.post1"
-s3fs = "^2023.12.2"
-adlfs = "^2023.12.0"
-httpx = "^0.26.0"
-inject = "^5.2.0"
-fastapi-restful = "^0.5.0"
-pydantic = "^2.6.3"
-pydantic-settings = "^2.2.1"
-typing-inspect = "^0.9.0"
-tqdm = "^4.66.3" # Dependency of superlinked. Here because of security vulnerability [FAI-1841]
-superlinked = {version = "^6.10.0", extras = ["redis", "mongo"]}
-
-[tool.poetry.group.dev.dependencies]
-typing-extensions = "^4.9.0"
-pytest = "^7.4.3"
-pytest-mock = "^3.12.0"
-pytest-emoji = "^0.2.0"
-pytest-md = "^0.2.0"
-pytest-asyncio = "^0.23.6"
-moto = {version = "^5.0.6", extras = ["all"]}
-jinja2 = "^3.1.4" # Dependency of moto. Here because of security vulnerability [FAI-1841]
-werkzeug = "^3.0.3" # Dependency of moto. Here because of security vulnerability [FAI-1841]
-types-google-cloud-ndb = "^2.2.0.20240205"
-types-jmespath = "^1.0.2.20240106"
-types-requests = "^2.27.1"
-types-pyyaml = "^6.0.12.12"
-mypy-boto3 = "^1.34.52"
-mypy = "<1.6"
-typing-protocol-intersection = "^0.4.0"
-ruff = "^0.4.2"
-pylint = "^3.1.0"
-vermin = "^1.6.0"
-
-[build-system]
-requires = ["poetry-core>=1.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.mypy]
-implicit_optional = true
-disallow_untyped_defs = true
-disallow_incomplete_defs = true
-plugins = ["pydantic.mypy", "typing_protocol_intersection.mypy_plugin"]
-exclude = "(.*/)?test(/|$)"
-
-[[tool.mypy.overrides]]
-module = "cerberus.*"
-ignore_missing_imports = true
-
-[[tool.mypy.overrides]]
-module = "google.*"
-ignore_missing_imports = true
-
-[[tool.mypy.overrides]]
-module = "uvicorn.*"
-ignore_missing_imports = true
-
-[[tool.mypy.overrides]]
-module = "fsspec.*"
-ignore_missing_imports = true
-
-[[tool.mypy.overrides]]
-module = "pandas.*"
-ignore_missing_imports = true
-
-[tool.ruff]
-exclude = [
-    ".git",
-    ".mypy_cache",
-    ".pytest_cache",
-    ".ruff_cache",
-    ".tox",
-    ".venv",
-    ".vscode",
-    "__pypackages__",
-    "build",
-    "dist",
-]
-extend-exclude = ["test"]
-line-length = 120
-indent-width = 4
-target-version = "py310"
-
-[tool.ruff.lint]
-select = ["ALL"]
-ignore = [
-    "D1",
-    "D2",
-    "PTH",
-    "N818",
-    "ANN101",
-    "TD002",
-    "TD003",
-    "FIX002",
-]
-# These rules need to be ignore because of the formatter
-extend-ignore = ["ISC001", "COM812"]
-fixable = ["ALL"]
-unfixable = []
-
-[tool.ruff.format]
-quote-style = "double"
-indent-style = "space"
-skip-magic-trailing-comma = false
-line-ending = "auto"
-docstring-code-format = false
-docstring-code-line-length = "dynamic"
-
-[tool.pylint.MASTER]
-max-args = 6
-max-line-length = 120
-ignore = 'test'
-disable = [
-    "missing-module-docstring",
-    "missing-class-docstring",
-    "missing-function-docstring",
-    "too-few-public-methods",
-    "protected-access",
-    "fixme",
-]
diff --git a/src/bonus_superlinked_rag/server/src/app.py b/src/bonus_superlinked_rag/server/src/app.py
deleted file mode 100644
index 7cc4d32..0000000
--- a/src/bonus_superlinked_rag/server/src/app.py
+++ /dev/null
@@ -1,191 +0,0 @@
-from pydantic_settings import BaseSettings
-from superlinked.framework.common.schema.id_schema_object import IdField
-from superlinked.framework.common.schema.schema import schema
-from superlinked.framework.common.schema.schema_object import String
-from superlinked.framework.dsl.executor.rest.rest_configuration import RestQuery
-from superlinked.framework.dsl.executor.rest.rest_descriptor import RestDescriptor
-from superlinked.framework.dsl.executor.rest.rest_executor import RestExecutor
-from superlinked.framework.dsl.index.index import Index
-from superlinked.framework.dsl.query.param import Param
-from superlinked.framework.dsl.query.query import Query
-from superlinked.framework.dsl.registry.superlinked_registry import SuperlinkedRegistry
-from superlinked.framework.dsl.source.rest_source import RestSource
-from superlinked.framework.dsl.space.categorical_similarity_space import (
-    CategoricalSimilaritySpace,
-)
-from superlinked.framework.dsl.space.text_similarity_space import (
-    TextSimilaritySpace,
-    chunk,
-)
-from superlinked.framework.dsl.storage.redis_vector_database import RedisVectorDatabase
-
-
-class Settings(BaseSettings):
-    EMBEDDING_MODEL_ID: str = "BAAI/bge-small-en-v1.5"
-
-    REDIS_HOSTNAME: str = "redis"
-    REDIS_PORT: int = 6379
-
-
-settings = Settings()
-
-
-# ----------------- SCHEMAS ----------------- #
-
-
-@schema
-class PostSchema:
-    id: IdField
-    platform: String
-    content: String
-    author_id: String
-    type: String
-
-
-@schema
-class ArticleSchema:
-    id: IdField
-    platform: String
-    link: String
-    content: String
-    author_id: String
-    type: String
-
-
-@schema
-class RepositorySchema:
-    id: IdField
-    platform: String
-    name: String
-    link: String
-    content: String
-    author_id: String
-    type: String
-
-
-post = PostSchema()
-article = ArticleSchema()
-repository = RepositorySchema()
-
-# ----------------- SPACES ----------------- #
-
-
-articles_space_content = TextSimilaritySpace(
-    text=chunk(article.content, chunk_size=500, chunk_overlap=50),
-    model=settings.EMBEDDING_MODEL_ID,
-)
-articles_space_plaform = CategoricalSimilaritySpace(
-    category_input=article.platform,
-    categories=["medium", "superlinked"],
-    negative_filter=-5.0,
-)
-
-repository_space_content = TextSimilaritySpace(
-    text=chunk(repository.content, chunk_size=1000, chunk_overlap=50),
-    model=settings.EMBEDDING_MODEL_ID,
-)
-repository_space_plaform = CategoricalSimilaritySpace(
-    category_input=repository.platform,
-    categories=["github", "gitlab"],
-    negative_filter=-5.0,
-)
-
-post_space_content = TextSimilaritySpace(
-    text=chunk(post.content, chunk_size=300, chunk_overlap=50),
-    model=settings.EMBEDDING_MODEL_ID,
-)
-post_space_plaform = CategoricalSimilaritySpace(
-    category_input=post.platform,
-    categories=["linkedin", "twitter"],
-    negative_filter=-5.0,
-)
-
-
-# ----------------- INDEXES ----------------- #
-
-article_index = Index(
-    [articles_space_content, articles_space_plaform],
-    fields=[article.author_id],
-)
-repository_index = Index(
-    [repository_space_content, repository_space_plaform],
-    fields=[repository.author_id],
-)
-post_index = Index(
-    [post_space_content, post_space_plaform],
-    fields=[post.author_id],
-)
-
-# ----------------- QUERIES ----------------- #
-
-post_query = (
-    Query(
-        post_index,
-        weights={
-            post_space_content: Param("content_weight"),
-            post_space_plaform: Param("platform_weight"),
-        },
-    )
-    .find(post)
-    .similar(post_space_content.text, Param("search_query"))
-    .similar(post_space_plaform.category, Param("platform"))
-    .filter(post.author_id == Param("author_id"))
-    .limit(Param("limit"))
-)
-article_query = (
-    Query(
-        article_index,
-        weights={
-            articles_space_content: Param("content_weight"),
-            articles_space_plaform: Param("platform_weight"),
-        },
-    )
-    .find(article)
-    .similar(articles_space_content.text, Param("search_query"))
-    .similar(articles_space_plaform.category, Param("platform"))
-    .filter(article.author_id == Param("author_id"))
-    .limit(Param("limit"))
-)
-repository_query = (
-    Query(
-        repository_index,
-        weights={
-            repository_space_content: Param("content_weight"),
-            repository_space_plaform: Param("platform_weight"),
-        },
-    )
-    .find(repository)
-    .similar(repository_space_content.text, Param("search_query"))
-    .similar(repository_space_plaform.category, Param("platform"))
-    .filter(repository.author_id == Param("author_id"))
-    .limit(Param("limit"))
-)
-
-# ----------------- SOURCES ----------------- #
-
-article_source = RestSource(article)
-repository_source = RestSource(repository)
-post_source = RestSource(post)
-
-
-# ----------------- VECTOR DB ----------------- #
-
-vector_database = RedisVectorDatabase(
-    settings.REDIS_HOSTNAME,  # (Mandatory) This is your redis URL without any port or extra fields
-    settings.REDIS_PORT,  # (Mandatory) This is the port and it should be an integer
-)
-
-
-# ----------------- EXECUTOR ----------------- #
-
-executor = RestExecutor(
-    sources=[article_source, repository_source, post_source],
-    indices=[article_index, repository_index, post_index],
-    queries=[
-        RestQuery(RestDescriptor("article_query"), article_query),
-        RestQuery(RestDescriptor("repository_query"), repository_query),
-        RestQuery(RestDescriptor("post_query"), post_query),
-    ],
-    vector_database=vector_database,
-)
-SuperlinkedRegistry.register(executor)
diff --git a/src/bonus_superlinked_rag/server/tools/deploy.py b/src/bonus_superlinked_rag/server/tools/deploy.py
deleted file mode 100755
index c9d76f1..0000000
--- a/src/bonus_superlinked_rag/server/tools/deploy.py
+++ /dev/null
@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2024 Superlinked, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""This module provides a script for deploying with docker-compose.
-
-Usage:
-    python deploy.py up       # Starts the services in detached mode
-    python deploy.py down     # Stops the services
-    python deploy.py restart  # Restarts all services
-"""
-import argparse
-import logging
-import os
-import select
-import subprocess
-import sys
-
-import yaml
-from cerberus import Validator
-
-config_schema = {
-    "app_location": {
-        "anyof": [
-            {"type": "string", "allowed": ["local"]},
-            {"type": "string", "regex": r"s3[a|n]?://([^/]+)/(.+)"},
-            {"type": "string", "regex": r"gs://([^/]+)/(.+)"},
-            {
-                "type": "string",
-                "regex": r"https?://(?:s3)[^/]*\.amazonaws\.com/([^/]+)/(.+)",
-            },
-            {
-                "type": "string",
-                "regex": r"https?://(?:storage\.cloud\.google\.com|storage\.googleapis\.com)/([^/]+)/(.+)",
-            },
-        ],
-        "required": True,
-    }
-}
-
-
-class NoOutputStreamsError(Exception):
-    pass
-
-
-def validate_config(config_path: str) -> bool:
-    if not os.path.isfile(config_path):
-        logging.error("Config file not found at path %s", config_path)
-        return False
-    validator = Validator(config_schema)
-    with open(config_path, encoding="utf-8") as fd:
-        config = yaml.safe_load(fd)
-    if validator.validate(config):
-        return True
-    logging.error("There was an error with the provided app.py location configuration.")
-    logging.error("Please make sure to use the full file URI in the configuration file (config/config.yaml)!")
-    logging.error("The following error(s) were found while validating the passed config file:")
-    logging.error(validator.errors)
-    return False
-
-
-def execute_command(command: list, cwd: str = None) -> None:
-    if cwd is None:
-        cwd = os.getcwd()
-
-    with subprocess.Popen(
-        command,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE,
-        bufsize=1,
-        universal_newlines=True,
-        cwd=cwd,
-    ) as process:
-        if not process.stdout or not process.stderr:
-            raise NoOutputStreamsError("Subprocess seems to have spawned without stdout and stderr")
-        fds = [process.stdout.fileno(), process.stderr.fileno()]
-        stream_map = {
-            process.stdout.fileno(): sys.stdout,
-            process.stderr.fileno(): sys.stderr,
-        }
-        while True:
-            if process.poll() is not None:
-                for line in process.stdout:
-                    sys.stdout.write(line)
-                for line in process.stderr:
-                    sys.stderr.write(line)
-                break
-
-            readable, _, _ = select.select(fds, [], [], 0.1)
-            for fd in readable:
-                line = os.read(fd, 1024).decode("utf-8")
-                stream_map[fd].write(line)
-
-    if process.returncode != 0:
-        raise subprocess.CalledProcessError(process.returncode, command)
-
-
-def run_docker_compose(command: str) -> None:
-    """
-    Run a docker-compose command.
-
-    Args:
-        command (str): The docker-compose command to run. Supported commands are 'up', 'down', and 'restart'.
-
-    Raises:
-        Exception: If the subprocess call returns a non-zero exit code.
-    """
-    match command:
-        case "up":
-            execute_command(["docker", "compose", command, "--build", "-d"])
-        case _:
-            execute_command(["docker", "compose", command])
-
-
-def print_usage() -> None:
-    """
-    Print the usage instructions for the deploy script.
-    """
-    print("Usage: python deploy.py [up|down|restart]")
-
-
-def main() -> None:
-    """
-    Parse arguments and execute the docker-compose command.
-    """
-    parser = argparse.ArgumentParser(description="Deploy services using docker-compose.")
-    parser.add_argument(
-        "command",
-        choices=["up", "down", "restart"],
-        help="The docker-compose command to run",
-    )
-
-    parser.add_argument(
-        "--config",
-        type=str,
-        default="config/config.yaml",
-        help="Path to the configuration file",
-    )
-
-    args = parser.parse_args()
-
-    match args.command:
-        case "up" | "restart":
-            if validate_config(args.config):
-                run_docker_compose(args.command)
-            else:
-                logging.error("The passed config file was invalid. Refusing to run.")
-        case "down":
-            run_docker_compose(args.command)
-        case _:
-            parser.print_help()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/bonus_superlinked_rag/server/tools/init-venv.sh b/src/bonus_superlinked_rag/server/tools/init-venv.sh
deleted file mode 100755
index 34f94e1..0000000
--- a/src/bonus_superlinked_rag/server/tools/init-venv.sh
+++ /dev/null
@@ -1,26 +0,0 @@
-#!/bin/bash
-# please install pyenv, poetry on your system
-# on Mac
-# brew install pyenv
-# brew install poetry
-
-set -euxo pipefail
-
-script_path=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)
-cd "${script_path}/../runner/"
-
-eval "$(pyenv init --path)"
-
-python_version=$(cat ../.python-version)
-
-if ! pyenv versions | grep -q "${python_version}"; then
-    echo y | pyenv install "${python_version}"
-fi
-
-poetry env use $(pyenv local ${python_version} && pyenv which python)
-
-poetry cache list | awk '{print $1}' | xargs -I {} poetry cache clear {} --all
-
-poetry install --no-root --with poller,executor,dev
-
-source "$(poetry env info --path)/bin/activate"
diff --git a/src/bonus_superlinked_rag/singleton.py b/src/bonus_superlinked_rag/singleton.py
deleted file mode 100644
index 29d768b..0000000
--- a/src/bonus_superlinked_rag/singleton.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from threading import Lock
-from typing import ClassVar
-
-
-class SingletonMeta(type):
-    """
-    This is a thread-safe implementation of Singleton.
-    """
-
-    _instances: ClassVar = {}
-
-    _lock: Lock = Lock()
-
-    """
-    We now have a lock object that will be used to synchronize threads during
-    first access to the Singleton.
-    """
-
-    def __call__(cls, *args, **kwargs):
-        """
-        Possible changes to the value of the `__init__` argument do not affect
-        the returned instance.
-        """
-        # Now, imagine that the program has just been launched. Since there's no
-        # Singleton instance yet, multiple threads can simultaneously pass the
-        # previous conditional and reach this point almost at the same time. The
-        # first of them will acquire lock and will proceed further, while the
-        # rest will wait here.
-        with cls._lock:
-            # The first thread to acquire the lock, reaches this conditional,
-            # goes inside and creates the Singleton instance. Once it leaves the
-            # lock block, a thread that might have been waiting for the lock
-            # release may then enter this section. But since the Singleton field
-            # is already initialized, the thread won't create a new object.
-            if cls not in cls._instances:
-                instance = super().__call__(*args, **kwargs)
-                cls._instances[cls] = instance
-
-        return cls._instances[cls]
diff --git a/src/bonus_superlinked_rag/superlinked_client.py b/src/bonus_superlinked_rag/superlinked_client.py
deleted file mode 100644
index 3dd2143..0000000
--- a/src/bonus_superlinked_rag/superlinked_client.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from typing import TypeVar
-
-import httpx
-
-from config import settings
-from models.documents import ArticleDocument, PostDocument, RepositoryDocument
-from utils.logging import get_logger
-
-logger = get_logger(__name__)
-
-
-T = TypeVar("T", ArticleDocument, PostDocument, RepositoryDocument)
-
-
-class SuperlinkedClient:
-    def __init__(self, base_url=settings.SUPERLINKED_SERVER_URL) -> None:
-        self.base_url = base_url
-        self.timeout = 600
-        self.headers = {"Accept": "*/*", "Content-Type": "application/json"}
-
-        self._content_weight = 0.9
-        self._platform_weight = 0.1
-
-    def ingest_repository(self, data: RepositoryDocument) -> None:
-        self.__ingest(f"{self.base_url}/api/v1/ingest/repository_schema", data)
-
-    def ingest_post(self, data: PostDocument) -> None:
-        self.__ingest(f"{self.base_url}/api/v1/ingest/post_schema", data)
-
-    def ingest_article(self, data: ArticleDocument) -> None:
-        self.__ingest(f"{self.base_url}/api/v1/ingest/article_schema", data)
-
-    def __ingest(self, url: str, data: T) -> None:
-        logger.info(f"Sending {type(data)} {data.id} to Superlinked at {url}")
-
-        response = httpx.post(
-            url, headers=self.headers, json=data.model_dump(), timeout=self.timeout
-        )
-
-        if response.status_code != 202:
-            raise httpx.HTTPStatusError(
-                "Ingestion failed", request=response.request, response=response
-            )
-
-        logger.info(f"Successfully sent {type(data)} {data.id} to Superlinked at {url}")
-
-    def search_repository(
-        self, search_query: str, platform: str, author_id: str, *, limit: int = 3
-    ) -> list[RepositoryDocument]:
-        return self.__search(
-            f"{self.base_url}/api/v1/search/repository_query",
-            RepositoryDocument,
-            search_query,
-            platform,
-            author_id,
-            limit=limit,
-        )
-
-    def search_post(
-        self, search_query: str, platform: str, author_id: str, *, limit: int = 3
-    ) -> list[PostDocument]:
-        return self.__search(
-            f"{self.base_url}/api/v1/search/post_query",
-            PostDocument,
-            search_query,
-            platform,
-            author_id,
-            limit=limit,
-        )
-
-    def search_article(
-        self, search_query: str, platform: str, author_id: str, *, limit: int = 3
-    ) -> list[ArticleDocument]:
-        return self.__search(
-            f"{self.base_url}/api/v1/search/article_query",
-            ArticleDocument,
-            search_query,
-            platform,
-            author_id,
-            limit=limit,
-        )
-
-    def __search(
-        self,
-        url: str,
-        document_class: type[T],
-        search_query: str,
-        platform: str,
-        author_id: str,
-        *,
-        limit: int = 3,
-    ) -> list[T]:
-        data = {
-            "search_query": search_query,
-            "platform": platform,
-            "author_id": author_id,
-            "limit": limit,
-            "content_weight": self._content_weight,
-            "platform_weight": self._platform_weight,
-        }
-        logger.info(f"Searching Superlinked for {document_class.__name__} at: {url}")
-        response = httpx.post(
-            url, headers=self.headers, json=data, timeout=self.timeout
-        )
-
-        if response.status_code != 200:
-            raise httpx.HTTPStatusError(
-                "Search failed", request=response.request, response=response
-            )
-
-        parsed_results = []
-        for result in response.json()["results"]:
-            parsed_results.append(document_class(**result["obj"]))
-
-        logger.info(
-            f"Successfully retrieved {len(parsed_results)} {document_class.__name__} from Superlinked for at: {url}"
-        )
-
-        return parsed_results
diff --git a/src/bonus_superlinked_rag/utils/logging.py b/src/bonus_superlinked_rag/utils/logging.py
deleted file mode 100644
index a1d5f3b..0000000
--- a/src/bonus_superlinked_rag/utils/logging.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import structlog
-
-
-def get_logger(cls: str):
-    return structlog.get_logger().bind(cls=cls)
\ No newline at end of file
diff --git a/src/core/__init__.py b/src/core/__init__.py
deleted file mode 100644
index b33736e..0000000
--- a/src/core/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from . import db, logger_utils, opik_utils
-from .logger_utils import get_logger
-
-logger = get_logger(__file__)
-
-try:
-    from .opik_utils import configure_opik
-
-    configure_opik()
-except:
-    logger.warning("Could not configure Opik.")
-
-__all__ = ["get_logger", "logger_utils", "opik_utils", "db"]
diff --git a/src/core/aws/__init__.py b/src/core/aws/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/src/core/aws/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/src/core/aws/create_execution_role.py b/src/core/aws/create_execution_role.py
deleted file mode 100644
index 1a3a73f..0000000
--- a/src/core/aws/create_execution_role.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import json
-from pathlib import Path
-
-from core.logger_utils import get_logger
-
-logger = get_logger(__file__)
-
-try:
-    import boto3
-except ModuleNotFoundError:
-    logger.warning(
-        "Couldn't load AWS or SageMaker imports. Run 'poetry install --with aws' to support AWS."
-    )
-
-from core.config import settings
-
-
-def create_sagemaker_execution_role(role_name: str):
-    assert settings.AWS_REGION, "AWS_REGION is not set."
-    assert settings.AWS_ACCESS_KEY, "AWS_ACCESS_KEY is not set."
-    assert settings.AWS_SECRET_KEY, "AWS_SECRET_KEY is not set."
-
-    # Create IAM client
-    iam = boto3.client(
-        "iam",
-        region_name=settings.AWS_REGION,
-        aws_access_key_id=settings.AWS_ACCESS_KEY,
-        aws_secret_access_key=settings.AWS_SECRET_KEY,
-    )
-
-    # Define the trust relationship policy
-    trust_relationship = {
-        "Version": "2012-10-17",
-        "Statement": [
-            {
-                "Effect": "Allow",
-                "Principal": {"Service": "sagemaker.amazonaws.com"},
-                "Action": "sts:AssumeRole",
-            }
-        ],
-    }
-
-    try:
-        # Create the IAM role
-        role = iam.create_role(
-            RoleName=role_name,
-            AssumeRolePolicyDocument=json.dumps(trust_relationship),
-            Description="Execution role for SageMaker",
-        )
-
-        # Attach necessary policies
-        policies = [
-            "arn:aws:iam::aws:policy/AmazonSageMakerFullAccess",
-            "arn:aws:iam::aws:policy/AmazonS3FullAccess",
-            "arn:aws:iam::aws:policy/CloudWatchLogsFullAccess",
-            "arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess",
-        ]
-
-        for policy in policies:
-            iam.attach_role_policy(RoleName=role_name, PolicyArn=policy)
-
-        logger.info(f"Role '{role_name}' created successfully.")
-        logger.info(f"Role ARN: {role['Role']['Arn']}")
-
-        return role["Role"]["Arn"]
-
-    except iam.exceptions.EntityAlreadyExistsException:
-        logger.warning(f"Role '{role_name}' already exists. Fetching its ARN...")
-        role = iam.get_role(RoleName=role_name)
-
-        return role["Role"]["Arn"]
-
-
-if __name__ == "__main__":
-    role_arn = create_sagemaker_execution_role("SageMakerExecutionRoleLLM")
-    logger.info(role_arn)
-
-    # Save the role ARN to a file
-    with Path("sagemaker_execution_role.json").open("w") as f:
-        json.dump({"RoleArn": role_arn}, f)
-
-    logger.info("Role ARN saved to 'sagemaker_execution_role.json'")
diff --git a/src/core/aws/create_sagemaker_role.py b/src/core/aws/create_sagemaker_role.py
deleted file mode 100644
index 016a26d..0000000
--- a/src/core/aws/create_sagemaker_role.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import json
-from pathlib import Path
-
-from logger_utils import get_logger
-
-logger = get_logger(__file__)
-
-try:
-    import boto3
-except ModuleNotFoundError:
-    logger.warning(
-        "Couldn't load AWS or SageMaker imports. Run 'poetry install --with aws' to support AWS."
-    )
-
-from config import settings
-
-
-def create_sagemaker_user(username: str):
-    assert settings.AWS_REGION, "AWS_REGION is not set."
-    assert settings.AWS_ACCESS_KEY, "AWS_ACCESS_KEY is not set."
-    assert settings.AWS_SECRET_KEY, "AWS_SECRET_KEY is not set."
-
-    # Create IAM client
-    iam = boto3.client(
-        "iam",
-        region_name=settings.AWS_REGION,
-        aws_access_key_id=settings.AWS_ACCESS_KEY,
-        aws_secret_access_key=settings.AWS_SECRET_KEY,
-    )
-
-    # Create user
-    iam.create_user(UserName=username)
-
-    # Attach necessary policies
-    policies = [
-        "arn:aws:iam::aws:policy/AmazonSageMakerFullAccess",
-        "arn:aws:iam::aws:policy/AWSCloudFormationFullAccess",
-        "arn:aws:iam::aws:policy/IAMFullAccess",
-        "arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess",
-        "arn:aws:iam::aws:policy/AmazonS3FullAccess",
-    ]
-
-    for policy in policies:
-        iam.attach_user_policy(UserName=username, PolicyArn=policy)
-
-    # Create access key
-    response = iam.create_access_key(UserName=username)
-    access_key = response["AccessKey"]
-
-    logger.info(f"User '{username}' successfully created.")
-    logger.info("Access Key ID and Secret Access Key successfully created.")
-
-    return {
-        "AccessKeyId": access_key["AccessKeyId"],
-        "SecretAccessKey": access_key["SecretAccessKey"],
-    }
-
-
-if __name__ == "__main__":
-    new_user = create_sagemaker_user("sagemaker-deployer")
-
-    with Path("sagemaker_user_credentials.json").open("w") as f:
-        json.dump(new_user, f)
-
-logger.info("Credentials saved to 'sagemaker_user_credentials.json'")
diff --git a/src/core/config.py b/src/core/config.py
deleted file mode 100644
index 49729a8..0000000
--- a/src/core/config.py
+++ /dev/null
@@ -1,66 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-
-
-class AppSettings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    # MongoDB configs
-    MONGO_DATABASE_HOST: str = (
-        "mongodb://mongo1:30001,mongo2:30002,mongo3:30003/?replicaSet=my-replica-set"
-    )
-    MONGO_DATABASE_NAME: str = "twin"
-
-    # MQ config
-    RABBITMQ_DEFAULT_USERNAME: str = "guest"
-    RABBITMQ_DEFAULT_PASSWORD: str = "guest"
-    RABBITMQ_HOST: str = "mq"
-    RABBITMQ_PORT: int = 5673
-
-    # QdrantDB config
-    QDRANT_CLOUD_URL: str = "str"
-    QDRANT_DATABASE_HOST: str = "qdrant"
-    QDRANT_DATABASE_PORT: int = 6333
-    USE_QDRANT_CLOUD: bool = False
-    QDRANT_APIKEY: str | None = None
-
-    # OpenAI config
-    OPENAI_MODEL_ID: str = "gpt-4o-mini"
-    OPENAI_API_KEY: str | None = None
-
-    # CometML config
-    COMET_API_KEY: str | None = None
-    COMET_WORKSPACE: str | None = None
-    COMET_PROJECT: str = "llm-twin"
-
-    # AWS Authentication
-    AWS_REGION: str = "eu-central-1"
-    AWS_ACCESS_KEY: str | None = None
-    AWS_SECRET_KEY: str | None = None
-    AWS_ARN_ROLE: str | None = None
-
-    # LLM Model config
-    HUGGINGFACE_ACCESS_TOKEN: str | None = None
-    MODEL_ID: str = "pauliusztin/LLMTwin-Llama-3.1-8B"
-    DEPLOYMENT_ENDPOINT_NAME: str = "twin"
-
-    MAX_INPUT_TOKENS: int = 1536  # Max length of input text.
-    MAX_TOTAL_TOKENS: int = 2048  # Max length of the generation (including input text).
-    MAX_BATCH_TOTAL_TOKENS: int = 2048  # Limits the number of tokens that can be processed in parallel during the generation.
-
-    # Embeddings config
-    EMBEDDING_MODEL_ID: str = "BAAI/bge-small-en-v1.5"
-    EMBEDDING_MODEL_MAX_INPUT_LENGTH: int = 512
-    EMBEDDING_SIZE: int = 384
-    EMBEDDING_MODEL_DEVICE: str = "cpu"
-
-    def patch_localhost(self) -> None:
-        self.MONGO_DATABASE_HOST = "mongodb://localhost:30001,localhost:30002,localhost:30003/?replicaSet=my-replica-set"
-        self.QDRANT_DATABASE_HOST = "localhost"
-        self.RABBITMQ_HOST = "localhost"
-
-
-settings = AppSettings()
diff --git a/src/core/db/__init__.py b/src/core/db/__init__.py
deleted file mode 100644
index 93f8e3f..0000000
--- a/src/core/db/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from . import documents, mongo, qdrant
-
-__all__ = ["documents", "mongo", "qdrant"]
diff --git a/src/core/errors.py b/src/core/errors.py
deleted file mode 100644
index e0f5a3c..0000000
--- a/src/core/errors.py
+++ /dev/null
@@ -1,6 +0,0 @@
-class TwinBaseException(Exception):
-    pass
-
-
-class ImproperlyConfigured(TwinBaseException):
-    pass
diff --git a/src/core/logger_utils.py b/src/core/logger_utils.py
deleted file mode 100644
index 98fe9f5..0000000
--- a/src/core/logger_utils.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import structlog
-
-
-def get_logger(cls: str):
-    return structlog.get_logger().bind(cls=cls)
diff --git a/src/core/opik_utils.py b/src/core/opik_utils.py
deleted file mode 100644
index b336dfe..0000000
--- a/src/core/opik_utils.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import json
-import os
-import random
-import tempfile
-from pathlib import Path
-
-from tqdm import tqdm
-
-from core.config import settings
-from core.logger_utils import get_logger
-
-logger = get_logger(__name__)
-
-try:
-    import opik
-    from comet_ml import Experiment
-    from opik.configurator.configure import OpikConfigurator
-except:
-    logger.info("Could not import Opik and Comet.")
-
-
-def configure_opik() -> None:
-    if settings.COMET_API_KEY and settings.COMET_PROJECT:
-        if settings.COMET_WORKSPACE:
-            default_workspace = settings.COMET_WORKSPACE
-        else:
-            try:
-                client = OpikConfigurator(api_key=settings.COMET_API_KEY)
-                default_workspace = client._get_default_workspace()
-            except Exception:
-                logger.warning(
-                    "Default workspace not found. Setting workspace to None and enabling interactive mode."
-                )
-                default_workspace = None
-
-        os.environ["OPIK_PROJECT_NAME"] = settings.COMET_PROJECT
-
-        opik.configure(
-            api_key=settings.COMET_API_KEY,
-            workspace=default_workspace,
-            use_local=False,
-            force=True,
-        )
-        logger.info("Opik configured successfully.")
-    else:
-        logger.warning(
-            "COMET_API_KEY and COMET_PROJECT are not set. Set them to enable prompt monitoring with Opik (powered by Comet ML)."
-        )
-
-
-def create_dataset_from_artifacts(
-    dataset_name: str, artifact_names: list[str]
-) -> opik.Dataset | None:
-    client = opik.Opik()
-    try:
-        dataset = client.get_dataset(name=dataset_name)
-    except Exception:
-        dataset = None
-
-    if dataset:
-        logger.warning(
-            f"Dataset '{dataset_name}' already exists. Skipping dataset creation."
-        )
-
-        return dataset
-
-    experiment = Experiment(
-        workspace=settings.COMET_WORKSPACE,
-        project_name=settings.COMET_PROJECT,
-        api_key=settings.COMET_API_KEY,
-    )
-    dataset_items = []
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        for artifact_name in tqdm(artifact_names):
-            artifact_dir = Path(tmp_dir) / artifact_name
-            try:
-                logged_artifact = experiment.get_artifact(artifact_name)
-                logged_artifact.download(str(artifact_dir))
-                logger.info(
-                    f"Successfully downloaded  '{artifact_name}' at location '{tmp_dir}'"
-                )
-            except Exception as e:
-                logger.error(f"Error retrieving artifact: {str(e)}")
-
-                continue
-
-            testing_artifact_file = list(artifact_dir.glob("*_testing.json"))
-            assert (
-                len(testing_artifact_file) == 1
-            ), "Expected exactly one testing artifact file."
-            testing_artifact_file = testing_artifact_file[0]
-
-            logger.info(f"Loading testing data from: {testing_artifact_file}")
-            with open(testing_artifact_file, "r") as file:
-                items = json.load(file)
-
-            enhanced_items = [
-                {**item, "artifact_name": artifact_name} for item in items
-            ]
-            dataset_items.extend(enhanced_items)
-    experiment.end()
-
-    if len(dataset_items) == 0:
-        logger.warning("No items found in the artifacts. Dataset creation skipped.")
-
-        return None
-
-    dataset = create_dataset(
-        name=dataset_name,
-        description="Dataset created from artifacts",
-        items=dataset_items,
-    )
-
-    return dataset
-
-
-def create_dataset(name: str, description: str, items: list[dict]) -> opik.Dataset:
-    client = opik.Opik()
-
-    dataset = client.get_or_create_dataset(name=name, description=description)
-    dataset.insert(items)
-
-    dataset = client.get_dataset(name=name)
-
-    return dataset
-
-
-def add_to_dataset_with_sampling(item: dict, dataset_name: str) -> bool:
-    if "1" in random.choices(["0", "1"], weights=[0.3, 0.7]):
-        client = opik.Opik()
-        dataset = client.get_or_create_dataset(name=dataset_name)
-        dataset.insert([item])
-
-        return True
-
-    return False
diff --git a/src/core/rag/__init__.py b/src/core/rag/__init__.py
deleted file mode 100644
index fe468a0..0000000
--- a/src/core/rag/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .query_expanison import QueryExpansion
\ No newline at end of file
diff --git a/src/core/rag/prompt_templates.py b/src/core/rag/prompt_templates.py
deleted file mode 100644
index f6ae3a7..0000000
--- a/src/core/rag/prompt_templates.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from abc import ABC, abstractmethod
-
-from langchain.prompts import PromptTemplate
-from pydantic import BaseModel
-
-
-class BasePromptTemplate(ABC, BaseModel):
-    @abstractmethod
-    def create_template(self, *args) -> PromptTemplate:
-        pass
-
-
-class QueryExpansionTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to generate {to_expand_to_n}
-    different versions of the given user question to retrieve relevant documents from a vector
-    database. By generating multiple perspectives on the user question, your goal is to help
-    the user overcome some of the limitations of the distance-based similarity search.
-    Provide these alternative questions separated by '{separator}'.
-    Original question: {question}"""
-
-    @property
-    def separator(self) -> str:
-        return "#next-question#"
-
-    def create_template(self, to_expand_to_n: int) -> PromptTemplate:
-        return PromptTemplate(
-            template=self.prompt,
-            input_variables=["question"],
-            partial_variables={
-                "separator": self.separator,
-                "to_expand_to_n": to_expand_to_n,
-            },
-        )
-
-
-class SelfQueryTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to extract information from a user question.
-    The required information that needs to be extracted is the user name or user id. 
-    Your response should consists of only the extracted user name (e.g., John Doe) or id (e.g. 1345256), nothing else.
-    If the user question does not contain any user name or id, you should return the following token: none.
-    
-    For example:
-    QUESTION 1:
-    My name is Paul Iusztin and I want a post about...
-    RESPONSE 1:
-    Paul Iusztin
-    
-    QUESTION 2:
-    I want to write a post about...
-    RESPONSE 2:
-    none
-    
-    QUESTION 3:
-    My user id is 1345256 and I want to write a post about...
-    RESPONSE 3:
-    1345256
-    
-    User question: {question}"""
-
-    def create_template(self) -> PromptTemplate:
-        return PromptTemplate(template=self.prompt, input_variables=["question"])
-
-
-class RerankingTemplate(BasePromptTemplate):
-    prompt: str = """You are an AI language model assistant. Your task is to rerank passages related to a query
-    based on their relevance. 
-    The most relevant passages should be put at the beginning. 
-    You should only pick at max {keep_top_k} passages.
-    The provided and reranked documents are separated by '{separator}'.
-    
-    The following are passages related to this query: {question}.
-    
-    Passages: 
-    {passages}
-    """
-
-    def create_template(self, keep_top_k: int) -> PromptTemplate:
-        return PromptTemplate(
-            template=self.prompt,
-            input_variables=["question", "passages"],
-            partial_variables={"keep_top_k": keep_top_k, "separator": self.separator},
-        )
-
-    @property
-    def separator(self) -> str:
-        return "\n#next-document#\n"
diff --git a/src/core/rag/query_expanison.py b/src/core/rag/query_expanison.py
deleted file mode 100644
index 67695ba..0000000
--- a/src/core/rag/query_expanison.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import opik
-from config import settings
-from langchain_openai import ChatOpenAI
-from opik.integrations.langchain import OpikTracer
-
-from core.rag.prompt_templates import QueryExpansionTemplate
-
-
-class QueryExpansion:
-    opik_tracer = OpikTracer(tags=["QueryExpansion"])
-
-    @staticmethod
-    @opik.track(name="QueryExpansion.generate_response")
-    def generate_response(query: str, to_expand_to_n: int) -> list[str]:
-        query_expansion_template = QueryExpansionTemplate()
-        prompt = query_expansion_template.create_template(to_expand_to_n)
-        model = ChatOpenAI(
-            model=settings.OPENAI_MODEL_ID,
-            api_key=settings.OPENAI_API_KEY,
-            temperature=0,
-        )
-        chain = prompt | model
-        chain = chain.with_config({"callbacks": [QueryExpansion.opik_tracer]})
-
-        response = chain.invoke({"question": query})
-        response = response.content
-
-        queries = response.strip().split(query_expansion_template.separator)
-        stripped_queries = [
-            stripped_item for item in queries if (stripped_item := item.strip(" \\n"))
-        ]
-
-        return stripped_queries
diff --git a/src/core/rag/reranking.py b/src/core/rag/reranking.py
deleted file mode 100644
index 8dbb8b9..0000000
--- a/src/core/rag/reranking.py
+++ /dev/null
@@ -1,33 +0,0 @@
-from config import settings
-from langchain_openai import ChatOpenAI
-
-from core.rag.prompt_templates import RerankingTemplate
-
-
-class Reranker:
-    @staticmethod
-    def generate_response(
-        query: str, passages: list[str], keep_top_k: int
-    ) -> list[str]:
-        reranking_template = RerankingTemplate()
-        prompt = reranking_template.create_template(keep_top_k=keep_top_k)
-        model = ChatOpenAI(
-            model=settings.OPENAI_MODEL_ID, api_key=settings.OPENAI_API_KEY
-        )
-        chain = prompt | model
-
-        stripped_passages = [
-            stripped_item for item in passages if (stripped_item := item.strip())
-        ]
-        passages = reranking_template.separator.join(stripped_passages)
-        response = chain.invoke({"question": query, "passages": passages})
-        response = response.content
-
-        reranked_passages = response.strip().split(reranking_template.separator)
-        stripped_passages = [
-            stripped_item
-            for item in reranked_passages
-            if (stripped_item := item.strip())
-        ]
-
-        return stripped_passages
diff --git a/src/core/rag/retriever.py b/src/core/rag/retriever.py
deleted file mode 100644
index 22b7437..0000000
--- a/src/core/rag/retriever.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import concurrent.futures
-
-import opik
-from config import settings
-from qdrant_client import models
-from sentence_transformers.SentenceTransformer import SentenceTransformer
-
-import core.logger_utils as logger_utils
-from core import lib
-from core.db.qdrant import QdrantDatabaseConnector
-from core.rag.query_expanison import QueryExpansion
-from core.rag.reranking import Reranker
-from core.rag.self_query import SelfQuery
-
-logger = logger_utils.get_logger(__name__)
-
-
-class VectorRetriever:
-    """
-    Class for retrieving vectors from a Vector store in a RAG system using query expansion and Multitenancy search.
-    """
-
-    def __init__(self, query: str) -> None:
-        self._client = QdrantDatabaseConnector()
-        self.query = query
-        self._embedder = SentenceTransformer(settings.EMBEDDING_MODEL_ID)
-        self._query_expander = QueryExpansion()
-        self._metadata_extractor = SelfQuery()
-        self._reranker = Reranker()
-
-    def _search_single_query(self, generated_query: str, author_id: str, k: int):
-        assert k > 3, "k should be greater than 3"
-
-        query_vector = self._embedder.encode(generated_query).tolist()
-
-        vectors = [
-            self._client.search(
-                collection_name="vector_posts",
-                query_filter=models.Filter(
-                    must=[
-                        models.FieldCondition(
-                            key="author_id",
-                            match=models.MatchValue(
-                                value=author_id,
-                            ),
-                        )
-                    ]
-                    if author_id
-                    else None
-                ),
-                query_vector=query_vector,
-                limit=k // 3,
-            ),
-            self._client.search(
-                collection_name="vector_articles",
-                query_filter=models.Filter(
-                    must=[
-                        models.FieldCondition(
-                            key="author_id",
-                            match=models.MatchValue(
-                                value=author_id,
-                            ),
-                        )
-                    ]
-                    if author_id
-                    else None
-                ),
-                query_vector=query_vector,
-                limit=k // 3,
-            ),
-            self._client.search(
-                collection_name="vector_repositories",
-                query_filter=models.Filter(
-                    must=[
-                        models.FieldCondition(
-                            key="owner_id",
-                            match=models.MatchValue(
-                                value=author_id,
-                            ),
-                        )
-                    ]
-                    if author_id
-                    else None
-                ),
-                query_vector=query_vector,
-                limit=k // 3,
-            ),
-        ]
-
-        return lib.flatten(vectors)
-
-    @opik.track(name="retriever.retrieve_top_k")
-    def retrieve_top_k(self, k: int, to_expand_to_n_queries: int) -> list:
-        generated_queries = self._query_expander.generate_response(
-            self.query, to_expand_to_n=to_expand_to_n_queries
-        )
-        logger.info(
-            "Successfully generated queries for search.",
-            num_queries=len(generated_queries),
-        )
-
-        author_id = self._metadata_extractor.generate_response(self.query)
-        if author_id:
-            logger.info(
-                "Successfully extracted the author_id from the query.",
-                author_id=author_id,
-            )
-        else:
-            logger.warning("Did not found any author data in the user's prompt.")
-
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            search_tasks = [
-                executor.submit(self._search_single_query, query, author_id, k)
-                for query in generated_queries
-            ]
-
-            hits = [
-                task.result() for task in concurrent.futures.as_completed(search_tasks)
-            ]
-            hits = lib.flatten(hits)
-
-        logger.info("All documents retrieved successfully.", num_documents=len(hits))
-
-        return hits
-
-    @opik.track(name="retriever.rerank")
-    def rerank(self, hits: list, keep_top_k: int) -> list[str]:
-        content_list = [hit.payload["content"] for hit in hits]
-        rerank_hits = self._reranker.generate_response(
-            query=self.query, passages=content_list, keep_top_k=keep_top_k
-        )
-
-        logger.info("Documents reranked successfully.", num_documents=len(rerank_hits))
-
-        return rerank_hits
-
-    def set_query(self, query: str):
-        self.query = query
diff --git a/src/core/rag/self_query.py b/src/core/rag/self_query.py
deleted file mode 100644
index 536ae08..0000000
--- a/src/core/rag/self_query.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import opik
-from config import settings
-from langchain_openai import ChatOpenAI
-from opik.integrations.langchain import OpikTracer
-
-import core.logger_utils as logger_utils
-from core import lib
-from core.db.documents import UserDocument
-from core.rag.prompt_templates import SelfQueryTemplate
-
-logger = logger_utils.get_logger(__name__)
-
-
-class SelfQuery:
-    opik_tracer = OpikTracer(tags=["SelfQuery"])
-
-    @staticmethod
-    @opik.track(name="SelQuery.generate_response")
-    def generate_response(query: str) -> str | None:
-        prompt = SelfQueryTemplate().create_template()
-        model = ChatOpenAI(
-            model=settings.OPENAI_MODEL_ID,
-            api_key=settings.OPENAI_API_KEY,
-            temperature=0,
-        )
-        chain = prompt | model
-        chain = chain.with_config({"callbacks": [SelfQuery.opik_tracer]})
-
-        response = chain.invoke({"question": query})
-        response = response.content
-        user_full_name = response.strip("\n ")
-
-        if user_full_name == "none":
-            return None
-
-        logger.info(
-            f"Successfully extracted the user full name from the query.",
-            user_full_name=user_full_name,
-        )
-        first_name, last_name = lib.split_user_full_name(user_full_name)
-        logger.info(
-            f"Successfully extracted the user first and last name from the query.",
-            first_name=first_name,
-            last_name=last_name,
-        )
-        user_id = UserDocument.get_or_create(first_name=first_name, last_name=last_name)
-
-        return user_id
diff --git a/src/data_cdc/config.py b/src/data_cdc/config.py
deleted file mode 100644
index 1574021..0000000
--- a/src/data_cdc/config.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    MONGO_DATABASE_HOST: str = (
-        "mongodb://mongo1:30001,mongo2:30002,mongo3:30003/?replicaSet=my-replica-set"
-    )
-    MONGO_DATABASE_NAME: str = "twin"
-
-    RABBITMQ_HOST: str = "mq"  # or localhost if running outside Docker
-    RABBITMQ_PORT: int = 5672
-    RABBITMQ_DEFAULT_USERNAME: str = "guest"
-    RABBITMQ_DEFAULT_PASSWORD: str = "guest"
-    RABBITMQ_QUEUE_NAME: str = "default"
-
-
-settings = Settings()
diff --git a/src/data_crawling/config.py b/src/data_crawling/config.py
deleted file mode 100644
index ca66f76..0000000
--- a/src/data_crawling/config.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    MONGO_DATABASE_HOST: str = (
-        "mongodb://mongo1:30001,mongo2:30002,mongo3:30003/?replicaSet=my-replica-set"
-    )
-    MONGO_DATABASE_NAME: str = "twin"
-
-    # Optional LinkedIn credentials for scraping your profile
-    LINKEDIN_USERNAME: str | None = None
-    LINKEDIN_PASSWORD: str | None = None
-
-
-settings = Settings()
diff --git a/src/data_crawling/crawlers/__init__.py b/src/data_crawling/crawlers/__init__.py
deleted file mode 100644
index 48a37e7..0000000
--- a/src/data_crawling/crawlers/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .custom_article import CustomArticleCrawler
-from .github import GithubCrawler
-from .linkedin import LinkedInCrawler
-from .medium import MediumCrawler
-
-__all__ = ["CustomArticleCrawler", "GithubCrawler", "LinkedInCrawler", "MediumCrawler"]
diff --git a/src/data_crawling/crawlers/custom_article.py b/src/data_crawling/crawlers/custom_article.py
deleted file mode 100644
index 08ee121..0000000
--- a/src/data_crawling/crawlers/custom_article.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from urllib.parse import urlparse
-
-from aws_lambda_powertools import Logger
-from core.db.documents import ArticleDocument
-from langchain_community.document_loaders import AsyncHtmlLoader
-from langchain_community.document_transformers.html2text import Html2TextTransformer
-
-from .base import BaseCrawler
-
-logger = Logger(service="llm-twin-course/crawler")
-
-
-class CustomArticleCrawler(BaseCrawler):
-    model = ArticleDocument
-
-    def __init__(self) -> None:
-        super().__init__()
-
-    def extract(self, link: str, **kwargs) -> None:
-        old_model = self.model.find(link=link)
-        if old_model is not None:
-            logger.info(f"Article already exists in the database: {link}")
-
-            return
-
-        logger.info(f"Starting scrapping article: {link}")
-
-        loader = AsyncHtmlLoader([link])
-        docs = loader.load()
-
-        html2text = Html2TextTransformer()
-        docs_transformed = html2text.transform_documents(docs)
-        doc_transformed = docs_transformed[0]
-
-        content = {
-            "Title": doc_transformed.metadata.get("title"),
-            "Subtitle": doc_transformed.metadata.get("description"),
-            "Content": doc_transformed.page_content,
-            "language": doc_transformed.metadata.get("language"),
-        }
-
-        parsed_url = urlparse(link)
-        platform = parsed_url.netloc
-
-        instance = self.model(
-            content=content,
-            link=link,
-            platform=platform,
-            author_id=kwargs.get("user"),
-        )
-        instance.save()
-
-        logger.info(f"Finished scrapping custom article: {link}")
diff --git a/src/data_crawling/utils.py b/src/data_crawling/utils.py
deleted file mode 100644
index 98fe9f5..0000000
--- a/src/data_crawling/utils.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import structlog
-
-
-def get_logger(cls: str):
-    return structlog.get_logger().bind(cls=cls)
diff --git a/src/feature_pipeline/config.py b/src/feature_pipeline/config.py
deleted file mode 100644
index 7c20d6a..0000000
--- a/src/feature_pipeline/config.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    # CometML config
-    COMET_API_KEY: str | None = None
-    COMET_WORKSPACE: str | None = None
-    COMET_PROJECT: str = "llm-twin"
-
-    # Embeddings config
-    EMBEDDING_MODEL_ID: str = "BAAI/bge-small-en-v1.5"
-    EMBEDDING_MODEL_MAX_INPUT_LENGTH: int = 512
-    EMBEDDING_SIZE: int = 384
-    EMBEDDING_MODEL_DEVICE: str = "cpu"
-
-    # OpenAI
-    OPENAI_MODEL_ID: str = "gpt-4o-mini"
-    OPENAI_API_KEY: str | None = None
-
-    # MQ config
-    RABBITMQ_DEFAULT_USERNAME: str = "guest"
-    RABBITMQ_DEFAULT_PASSWORD: str = "guest"
-    RABBITMQ_HOST: str = "mq"  # or localhost if running outside Docker
-    RABBITMQ_PORT: int = 5672
-    RABBITMQ_QUEUE_NAME: str = "default"
-
-    # QdrantDB config
-    QDRANT_DATABASE_HOST: str = "qdrant"  # or localhost if running outside Docker
-    QDRANT_DATABASE_PORT: int = 6333
-    USE_QDRANT_CLOUD: bool = (
-        False  # if True, fill in QDRANT_CLOUD_URL and QDRANT_APIKEY
-    )
-    QDRANT_CLOUD_URL: str | None = None
-    QDRANT_APIKEY: str | None = None
-
-
-settings = Settings()
diff --git a/src/feature_pipeline/data_flow/__init__.py b/src/feature_pipeline/data_flow/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/feature_pipeline/data_logic/__init__.py b/src/feature_pipeline/data_logic/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/feature_pipeline/generate_dataset/__init__.py b/src/feature_pipeline/generate_dataset/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/feature_pipeline/generate_dataset/chunk_documents.py b/src/feature_pipeline/generate_dataset/chunk_documents.py
deleted file mode 100644
index dca8747..0000000
--- a/src/feature_pipeline/generate_dataset/chunk_documents.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import re
-
-
-def chunk_documents(documents: list[str], min_length: int = 1000, max_length: int = 2000):
-    chunked_documents = []
-    for document in documents:
-        chunks = extract_substrings(document, min_length=min_length, max_length=max_length)
-        chunked_documents.extend(chunks)
-        
-    return chunked_documents
-
-def extract_substrings(
-    text: str, min_length: int = 1000, max_length: int = 2000
-) -> list[str]:
-    sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?|\!)\s", text)
-
-    extracts = []
-    current_chunk = ""
-    for sentence in sentences:
-        sentence = sentence.strip()
-        if not sentence:
-            continue
-
-        if len(current_chunk) + len(sentence) <= max_length:
-            current_chunk += sentence + " "
-        else:
-            if len(current_chunk) >= min_length:
-                extracts.append(current_chunk.strip())
-            current_chunk = sentence + " "
-
-    if len(current_chunk) >= min_length:
-        extracts.append(current_chunk.strip())
-
-    return extracts
diff --git a/src/feature_pipeline/generate_dataset/exceptions.py b/src/feature_pipeline/generate_dataset/exceptions.py
deleted file mode 100644
index 0edca09..0000000
--- a/src/feature_pipeline/generate_dataset/exceptions.py
+++ /dev/null
@@ -1,14 +0,0 @@
-class DatasetError(Exception):
-    pass
-
-
-class FileNotFoundError(DatasetError):
-    pass
-
-
-class JSONDecodeError(DatasetError):
-    pass
-
-
-class APICommunicationError(DatasetError):
-    pass
diff --git a/src/feature_pipeline/generate_dataset/file_handler.py b/src/feature_pipeline/generate_dataset/file_handler.py
deleted file mode 100644
index 5a6e92e..0000000
--- a/src/feature_pipeline/generate_dataset/file_handler.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import json
-
-from generate_dataset.exceptions import JSONDecodeError
-
-
-class FileHandler:
-    def read_json(self, filename: str) -> list:
-        try:
-            with open(filename, "r") as file:
-                return json.load(file)
-        except FileNotFoundError:
-            raise FileNotFoundError(f"The file '{filename}' does not exist.")
-        except json.JSONDecodeError:
-            raise JSONDecodeError(
-                f"The file '{filename}' is not properly formatted as JSON."
-            )
-
-    def write_json(self, filename: str, data: list):
-        with open(filename, "w") as file:
-            json.dump(data, file, indent=4)
diff --git a/src/feature_pipeline/generate_dataset/generate.py b/src/feature_pipeline/generate_dataset/generate.py
deleted file mode 100644
index b7c1e90..0000000
--- a/src/feature_pipeline/generate_dataset/generate.py
+++ /dev/null
@@ -1,231 +0,0 @@
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-sys.path.append(ROOT_DIR)
-
-
-from core import get_logger
-from core.config import settings
-
-logger = get_logger(__name__)
-
-settings.patch_localhost()
-logger.warning(
-    "Patched settings to work with 'localhost' URLs. \
-    Remove the 'settings.patch_localhost()' call from above when deploying or running inside Docker."
-)
-
-
-import json
-import logging
-from pathlib import Path
-
-from comet_ml import Artifact, start
-from core import get_logger
-from core.db.qdrant import QdrantDatabaseConnector
-from sklearn.model_selection import train_test_split
-
-from .chunk_documents import chunk_documents
-from .file_handler import FileHandler
-from .llm_communication import GptCommunicator
-
-logger = get_logger(__name__)
-
-
-client = QdrantDatabaseConnector()
-
-
-class DataFormatter:
-    @classmethod
-    def get_system_prompt(cls, data_type: str) -> str:
-        return (
-            f"I will give you batches of contents of {data_type}. Please generate me exactly 1 instruction for each of them. The {data_type} text "
-            f"for which you have to generate the instructions is under Content number x lines. Please structure the answer in json format,"
-            f"ready to be loaded by json.loads(), a list of objects only with fields called instruction and content. For the content field, copy the number of the content only!."
-            f"Please do not add any extra characters and make sure it is a list with objects in valid json format!\n"
-        )
-
-    @classmethod
-    def format_data(cls, data_points: list, is_example: bool, start_index: int) -> str:
-        text = ""
-        for index, data_point in enumerate(data_points):
-            if not is_example:
-                text += f"Content number {start_index + index }\n"
-            text += str(data_point) + "\n"
-
-        return text
-
-    @classmethod
-    def format_batch(cls, context_msg: str, data_points: list, start_index: int) -> str:
-        delimiter_msg = context_msg
-        delimiter_msg += cls.format_data(data_points, False, start_index)
-
-        return delimiter_msg
-
-    @classmethod
-    def format_prompt(
-        cls, inference_posts: list, data_type: str, start_index: int
-    ) -> str:
-        initial_prompt = cls.get_system_prompt(data_type)
-        initial_prompt += f"You must generate exactly a list of {len(inference_posts)} json objects, using the contents provided under CONTENTS FOR GENERATION\n"
-        initial_prompt += cls.format_batch(
-            "\nCONTENTS FOR GENERATION: \n", inference_posts, start_index
-        )
-
-        return initial_prompt
-
-
-class DatasetGenerator:
-    def __init__(
-        self,
-        file_handler: FileHandler,
-        api_communicator: GptCommunicator,
-        data_formatter: DataFormatter,
-    ) -> None:
-        self.file_handler = file_handler
-        self.api_communicator = api_communicator
-        self.data_formatter = data_formatter
-
-    def generate_training_data(
-        self, collection_name: str, data_type: str, batch_size: int = 3
-    ) -> None:
-        assert (
-            settings.COMET_API_KEY
-        ), "COMET_API_KEY must be set in settings, fill it in your .env file."
-        assert (
-            settings.COMET_WORKSPACE
-        ), "COMET_PROJECT must be set in settings, fill it in your .env file."
-        assert (
-            settings.COMET_WORKSPACE
-        ), "COMET_PROJECT must be set in settings, fill it in your .env file."
-        assert (
-            settings.OPENAI_API_KEY
-        ), "OPENAI_API_KEY must be set in settings, fill it in your .env file."
-
-        cleaned_documents = self.fetch_all_cleaned_content(collection_name)
-        cleaned_documents = chunk_documents(cleaned_documents)
-        num_cleaned_documents = len(cleaned_documents)
-
-        generated_instruct_dataset = []
-        for i in range(0, num_cleaned_documents, batch_size):
-            batch = cleaned_documents[i : i + batch_size]
-            prompt = data_formatter.format_prompt(batch, data_type, i)
-            batch_instructions = self.api_communicator.send_prompt(prompt)
-
-            if len(batch_instructions) != len(batch):
-                logger.error(
-                    f"Received {len(batch_instructions)} instructions for {len(batch)} documents. \
-                    Skipping this batch..."
-                )
-                continue
-
-            for instruction, content in zip(batch_instructions, batch):
-                instruction["content"] = content
-                generated_instruct_dataset.append(instruction)
-
-        train_test_split = self._split_dataset(generated_instruct_dataset)
-
-        self.push_to_comet(train_test_split, data_type, collection_name)
-
-    def _split_dataset(
-        self, generated_instruct_dataset: list[dict], test_size: float = 0.1
-    ) -> tuple[list[dict], list[dict]]:
-        """Split dataset into train and test sets.
-
-        Args:
-            generated_instruct_dataset (dict): Dataset containing content and instruction pairs
-
-        Returns:
-            tuple[dict, dict]: Train and test splits of the dataset
-        """
-
-        if len(generated_instruct_dataset) == 0:
-            return [], []
-
-        train_data, test_data = train_test_split(
-            generated_instruct_dataset, test_size=test_size, random_state=42
-        )
-
-        return train_data, test_data
-
-    def push_to_comet(
-        self,
-        train_test_split: tuple[list[dict], list[dict]],
-        data_type: str,
-        collection_name: str,
-        output_dir: Path = Path("generated_dataset"),
-    ) -> None:
-        output_dir.mkdir(exist_ok=True)
-
-        try:
-            logger.info(f"Starting to push data to Comet: {collection_name}")
-
-            experiment = start()
-
-            training_data, testing_data = train_test_split
-
-            file_name_training_data = output_dir / f"{collection_name}_training.json"
-            file_name_testing_data = output_dir / f"{collection_name}_testing.json"
-
-            logging.info(f"Writing training data to file: {file_name_training_data}")
-            with file_name_training_data.open("w") as f:
-                json.dump(training_data, f)
-
-            logging.info(f"Writing testing data to file: {file_name_testing_data}")
-            with file_name_testing_data.open("w") as f:
-                json.dump(testing_data, f)
-
-            logger.info("Data written to file successfully")
-
-            artifact = Artifact(f"{data_type}-instruct-dataset")
-            artifact.add(file_name_training_data)
-            artifact.add(file_name_testing_data)
-            logger.info(f"Artifact created.")
-
-            experiment.log_artifact(artifact)
-            experiment.end()
-            logger.info("Artifact pushed to Comet successfully.")
-
-        except Exception:
-            logger.exception(
-                f"Failed to create Comet artifact and push it to Comet.",
-            )
-
-    def fetch_all_cleaned_content(self, collection_name: str) -> list:
-        all_cleaned_contents = []
-
-        scroll_response = client.scroll(collection_name=collection_name, limit=10000)
-        points = scroll_response[0]
-
-        for point in points:
-            cleaned_content = point.payload["cleaned_content"]
-            if cleaned_content:
-                all_cleaned_contents.append(cleaned_content)
-
-        return all_cleaned_contents
-
-
-if __name__ == "__main__":
-    file_handler = FileHandler()
-    api_communicator = GptCommunicator()
-    data_formatter = DataFormatter()
-    dataset_generator = DatasetGenerator(file_handler, api_communicator, data_formatter)
-
-    collections = [
-        ("cleaned_articles", "articles"),
-        ("cleaned_repositories", "repositories"),
-    ]
-    for collection_name, data_type in collections:
-        logger.info(
-            "Generating training data.",
-            collection_name=collection_name,
-            data_type=data_type,
-        )
-
-        dataset_generator.generate_training_data(
-            collection_name=collection_name, data_type=data_type
-        )
diff --git a/src/feature_pipeline/generate_dataset/llm_communication.py b/src/feature_pipeline/generate_dataset/llm_communication.py
deleted file mode 100644
index fb12969..0000000
--- a/src/feature_pipeline/generate_dataset/llm_communication.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-
-from config import settings
-from core import get_logger
-from openai import OpenAI
-
-MAX_LENGTH = 16384
-SYSTEM_PROMPT = (
-    "You are a technical writer handing someone's account to post about AI and MLOps."
-)
-
-logger = get_logger(__name__)
-
-
-class GptCommunicator:
-    def __init__(self, gpt_model: str = settings.OPENAI_MODEL_ID):
-        self.api_key = settings.OPENAI_API_KEY
-        self.gpt_model = gpt_model
-
-    def send_prompt(self, prompt: str) -> list:
-        try:
-            client = OpenAI(api_key=self.api_key)
-            logger.info(f"Sending batch to GPT = '{settings.OPENAI_MODEL_ID}'.")
-
-            chat_completion = client.chat.completions.create(
-                messages=[
-                    {"role": "system", "content": SYSTEM_PROMPT},
-                    {"role": "user", "content": prompt[:MAX_LENGTH]},
-                ],
-                model=self.gpt_model,
-            )
-            response = chat_completion.choices[0].message.content
-            return json.loads(self.clean_response(response))
-        except Exception:
-            logger.exception(
-                f"Skipping batch! An error occurred while communicating with API."
-            )
-
-            return []
-
-    @staticmethod
-    def clean_response(response: str) -> str:
-        start_index = response.find("[")
-        end_index = response.rfind("]")
-        return response[start_index : end_index + 1]
diff --git a/src/feature_pipeline/models/__init__.py b/src/feature_pipeline/models/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/feature_pipeline/retriever.py b/src/feature_pipeline/retriever.py
deleted file mode 100644
index fc518a9..0000000
--- a/src/feature_pipeline/retriever.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent)
-sys.path.append(ROOT_DIR)
-
-
-from core import get_logger
-from core.config import settings
-from core.rag.retriever import VectorRetriever
-
-logger = get_logger(__name__)
-
-settings.patch_localhost()
-logger.warning(
-    "Patched settings to work with 'localhost' URLs. \
-    Remove the 'settings.patch_localhost()' call from above when deploying or running inside Docker."
-)
-
-if __name__ == "__main__":
-    query = """
-Hello I am Paul Iusztin.
-        
-Could you draft an article paragraph discussing RAG? 
-I'm particularly interested in how to design a RAG system.
-"""
-
-    retriever = VectorRetriever(query=query)
-    hits = retriever.retrieve_top_k(k=6, to_expand_to_n_queries=5)
-    reranked_hits = retriever.rerank(hits=hits, keep_top_k=5)
-
-    logger.info("====== RETRIEVED DOCUMENTS ======")
-    for rank, hit in enumerate(reranked_hits):
-        logger.info(f"Rank = {rank} : {hit}")
diff --git a/src/feature_pipeline/scripts/bytewax_entrypoint.sh b/src/feature_pipeline/scripts/bytewax_entrypoint.sh
deleted file mode 100644
index 545db5f..0000000
--- a/src/feature_pipeline/scripts/bytewax_entrypoint.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/sh
-
-if [ "$DEBUG" = true ]
-then
-    python -m bytewax.run "tools.run_real_time:build_flow(debug=True)"
-else
-    if [ "$BYTEWAX_PYTHON_FILE_PATH" = "" ]
-    then
-        echo 'BYTEWAX_PYTHON_FILE_PATH is not set. Exiting...'
-        exit 1
-    fi
-    python -m bytewax.run $BYTEWAX_PYTHON_FILE_PATH
-fi
-
-
-echo 'Process ended.'
-
-if [ "$BYTEWAX_KEEP_CONTAINER_ALIVE" = true ]
-then
-    echo 'Keeping container alive...';
-    while :; do sleep 1; done
-fi
\ No newline at end of file
diff --git a/src/feature_pipeline/utils/__init__.py b/src/feature_pipeline/utils/__init__.py
deleted file mode 100644
index 8b13789..0000000
--- a/src/feature_pipeline/utils/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/src/feature_pipeline/utils/cleaning.py b/src/feature_pipeline/utils/cleaning.py
deleted file mode 100644
index 8cc0fc7..0000000
--- a/src/feature_pipeline/utils/cleaning.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import re
-
-from unstructured.cleaners.core import (
-    clean,
-    clean_non_ascii_chars,
-    replace_unicode_quotes,
-)
-
-
-def unbold_text(text):
-    # Mapping of bold numbers to their regular equivalents
-    bold_numbers = {
-        "𝟬": "0",
-        "𝟭": "1",
-        "𝟮": "2",
-        "𝟯": "3",
-        "𝟰": "4",
-        "𝟱": "5",
-        "𝟲": "6",
-        "𝟳": "7",
-        "𝟴": "8",
-        "𝟵": "9",
-    }
-
-    # Function to convert bold characters (letters and numbers)
-    def convert_bold_char(match):
-        char = match.group(0)
-        # Convert bold numbers
-        if char in bold_numbers:
-            return bold_numbers[char]
-        # Convert bold uppercase letters
-        elif "\U0001d5d4" <= char <= "\U0001d5ed":
-            return chr(ord(char) - 0x1D5D4 + ord("A"))
-        # Convert bold lowercase letters
-        elif "\U0001d5ee" <= char <= "\U0001d607":
-            return chr(ord(char) - 0x1D5EE + ord("a"))
-        else:
-            return char  # Return the character unchanged if it's not a bold number or letter
-
-    # Regex for bold characters (numbers, uppercase, and lowercase letters)
-    bold_pattern = re.compile(
-        r"[\U0001D5D4-\U0001D5ED\U0001D5EE-\U0001D607\U0001D7CE-\U0001D7FF]"
-    )
-    text = bold_pattern.sub(convert_bold_char, text)
-
-    return text
-
-
-def unitalic_text(text):
-    # Function to convert italic characters (both letters)
-    def convert_italic_char(match):
-        char = match.group(0)
-        # Unicode ranges for italic characters
-        if "\U0001d608" <= char <= "\U0001d621":  # Italic uppercase A-Z
-            return chr(ord(char) - 0x1D608 + ord("A"))
-        elif "\U0001d622" <= char <= "\U0001d63b":  # Italic lowercase a-z
-            return chr(ord(char) - 0x1D622 + ord("a"))
-        else:
-            return char  # Return the character unchanged if it's not an italic letter
-
-    # Regex for italic characters (uppercase and lowercase letters)
-    italic_pattern = re.compile(r"[\U0001D608-\U0001D621\U0001D622-\U0001D63B]")
-    text = italic_pattern.sub(convert_italic_char, text)
-
-    return text
-
-
-def remove_emojis_and_symbols(text):
-    # Extended pattern to include specific symbols like ↓ (U+2193) or ↳ (U+21B3)
-    emoji_and_symbol_pattern = re.compile(
-        "["
-        "\U0001f600-\U0001f64f"  # emoticons
-        "\U0001f300-\U0001f5ff"  # symbols & pictographs
-        "\U0001f680-\U0001f6ff"  # transport & map symbols
-        "\U0001f1e0-\U0001f1ff"  # flags (iOS)
-        "\U00002193"  # downwards arrow
-        "\U000021b3"  # downwards arrow with tip rightwards
-        "\U00002192"  # rightwards arrow
-        "]+",
-        flags=re.UNICODE,
-    )
-
-    return emoji_and_symbol_pattern.sub(r" ", text)
-
-
-def replace_urls_with_placeholder(text, placeholder="[URL]"):
-    # Regular expression pattern for matching URLs
-    url_pattern = r"https?://\S+|www\.\S+"
-
-    return re.sub(url_pattern, placeholder, text)
-
-
-def remove_non_ascii(text: str) -> str:
-    text = text.encode("ascii", "ignore").decode("ascii")
-    return text
-
-
-def clean_text(text_content: str | None) -> str:
-    if text_content is None:
-        return ""
-
-    cleaned_text = unbold_text(text_content)
-    cleaned_text = unitalic_text(cleaned_text)
-    cleaned_text = remove_emojis_and_symbols(cleaned_text)
-    cleaned_text = clean(cleaned_text)
-    cleaned_text = replace_unicode_quotes(cleaned_text)
-    cleaned_text = clean_non_ascii_chars(cleaned_text)
-    cleaned_text = replace_urls_with_placeholder(cleaned_text)
-
-    return cleaned_text
diff --git a/src/inference_pipeline/aws/__init__.py b/src/inference_pipeline/aws/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/src/inference_pipeline/aws/delete_sagemaker_endpoint.py b/src/inference_pipeline/aws/delete_sagemaker_endpoint.py
deleted file mode 100644
index 3f6406c..0000000
--- a/src/inference_pipeline/aws/delete_sagemaker_endpoint.py
+++ /dev/null
@@ -1,80 +0,0 @@
-from core import get_logger
-
-logger = get_logger(__file__)
-
-try:
-    import boto3
-    from botocore.exceptions import ClientError
-except ModuleNotFoundError:
-    logger.warning(
-        "Couldn't load AWS or SageMaker imports. Run 'poetry install --with aws' to support AWS."
-    )
-
-
-from config import settings
-
-
-def delete_endpoint_and_config(endpoint_name) -> None:
-    """
-    Deletes an AWS SageMaker endpoint and its associated configuration.
-    Args:
-    endpoint_name (str): The name of the SageMaker endpoint to delete.
-    Returns:
-    None
-    """
-
-    try:
-        sagemaker_client = boto3.client(
-            "sagemaker",
-            region_name=settings.AWS_REGION,
-            aws_access_key_id=settings.AWS_ACCESS_KEY,
-            aws_secret_access_key=settings.AWS_SECRET_KEY,
-        )
-    except Exception:
-        logger.exception("Error creating SageMaker client")
-
-        return
-
-    # Get the endpoint configuration name
-    try:
-        response = sagemaker_client.describe_endpoint(EndpointName=endpoint_name)
-        config_name = response["EndpointConfigName"]
-    except ClientError:
-        logger.error("Error getting endpoint configuration and modelname.")
-
-        return
-
-    # Delete the endpoint
-    try:
-        sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
-        logger.info(f"Endpoint '{endpoint_name}' deletion initiated.")
-    except ClientError:
-        logger.error("Error deleting endpoint")
-
-    try:
-        response = sagemaker_client.describe_endpoint_config(
-            EndpointConfigName=endpoint_name
-        )
-        model_name = response["ProductionVariants"][0]["ModelName"]
-    except ClientError:
-        logger.error("Error getting model name.")
-
-    # Delete the endpoint configuration
-    try:
-        sagemaker_client.delete_endpoint_config(EndpointConfigName=config_name)
-        logger.info(f"Endpoint configuration '{config_name}' deleted.")
-    except ClientError:
-        logger.error("Error deleting endpoint configuration.")
-
-    # Delete models
-    try:
-        sagemaker_client.delete_model(ModelName=model_name)
-        logger.info(f"Model '{model_name}' deleted.")
-    except ClientError:
-        logger.error("Error deleting model.")
-
-
-if __name__ == "__main__":
-    endpoint_name = settings.DEPLOYMENT_ENDPOINT_NAME
-    logger.info(f"Attempting to delete endpoint: {endpoint_name}")
-    delete_endpoint_and_config(endpoint_name=endpoint_name)
diff --git a/src/inference_pipeline/aws/deploy_sagemaker_endpoint.py b/src/inference_pipeline/aws/deploy_sagemaker_endpoint.py
deleted file mode 100644
index 3816a0a..0000000
--- a/src/inference_pipeline/aws/deploy_sagemaker_endpoint.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from config import settings
-from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
-
-
-def main() -> None:
-    assert settings.HUGGINGFACE_ACCESS_TOKEN, "HUGGINGFACE_ACCESS_TOKEN is required."
-
-    env_vars = {
-        "HF_MODEL_ID": settings.MODEL_ID,
-        "SM_NUM_GPUS": "1",  # Number of GPU used per replica.
-        "HUGGING_FACE_HUB_TOKEN": settings.HUGGINGFACE_ACCESS_TOKEN,
-        "MAX_INPUT_TOKENS": str(
-            settings.MAX_INPUT_TOKENS
-        ),  # Max length of input tokens.
-        "MAX_TOTAL_TOKENS": str(
-            settings.MAX_TOTAL_TOKENS
-        ),  # Max length of the generation (including input text).
-        "MAX_BATCH_TOTAL_TOKENS": str(
-            settings.MAX_BATCH_TOTAL_TOKENS
-        ),  # Limits the number of tokens that can be processed in parallel during the generation.
-        "MESSAGES_API_ENABLED": "true",  # Enable/disable the messages API, following OpenAI's standard.
-        "HF_MODEL_QUANTIZE": "bitsandbytes",
-    }
-
-    image_uri = get_huggingface_llm_image_uri("huggingface", version="2.2.0")
-
-    model = HuggingFaceModel(
-        env=env_vars, role=settings.AWS_ARN_ROLE, image_uri=image_uri
-    )
-
-    model.deploy(
-        initial_instance_count=1,
-        instance_type="ml.g5.2xlarge",
-        container_startup_health_check_timeout=900,
-        endpoint_name=settings.DEPLOYMENT_ENDPOINT_NAME,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/inference_pipeline/config.py b/src/inference_pipeline/config.py
deleted file mode 100644
index 311962f..0000000
--- a/src/inference_pipeline/config.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    # Embeddings config
-    EMBEDDING_MODEL_ID: str = "BAAI/bge-small-en-v1.5"
-    EMBEDDING_MODEL_MAX_INPUT_LENGTH: int = 512
-    EMBEDDING_SIZE: int = 384
-    EMBEDDING_MODEL_DEVICE: str = "cpu"
-
-    # OpenAI config
-    OPENAI_MODEL_ID: str = "gpt-4o-mini"
-    OPENAI_API_KEY: str | None = None
-
-    # QdrantDB config
-    QDRANT_DATABASE_HOST: str = "localhost"  # Or 'qdrant' if running inside Docker
-    QDRANT_DATABASE_PORT: int = 6333
-
-    USE_QDRANT_CLOUD: bool = (
-        False  # if True, fill in QDRANT_CLOUD_URL and QDRANT_APIKEY
-    )
-    QDRANT_CLOUD_URL: str = "str"
-    QDRANT_APIKEY: str | None = None
-
-    # RAG config
-    TOP_K: int = 5
-    KEEP_TOP_K: int = 5
-    EXPAND_N_QUERY: int = 5
-
-    # CometML config
-    COMET_API_KEY: str
-    COMET_WORKSPACE: str
-    COMET_PROJECT: str = "llm-twin"
-
-    # LLM Model config
-    HUGGINGFACE_ACCESS_TOKEN: str | None = None
-    MODEL_ID: str = "pauliusztin/LLMTwin-Llama-3.1-8B" # Change this with your Hugging Face model ID to test out your fine-tuned LLM
-    DEPLOYMENT_ENDPOINT_NAME: str = "twin"
-
-    MAX_INPUT_TOKENS: int = 1536  # Max length of input text.
-    MAX_TOTAL_TOKENS: int = 2048  # Max length of the generation (including input text).
-    MAX_BATCH_TOTAL_TOKENS: int = 2048  # Limits the number of tokens that can be processed in parallel during the generation.
-
-    # AWS Authentication
-    AWS_REGION: str = "eu-central-1"
-    AWS_ACCESS_KEY: str | None = None
-    AWS_SECRET_KEY: str | None = None
-    AWS_ARN_ROLE: str | None = None
-
-
-settings = Settings()
diff --git a/src/inference_pipeline/evaluation/__init__.py b/src/inference_pipeline/evaluation/__init__.py
deleted file mode 100644
index 131b58d..0000000
--- a/src/inference_pipeline/evaluation/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent.parent)
-sys.path.append(ROOT_DIR)
-
-from core import logger_utils
-
-logger = logger_utils.get_logger(__name__)
-logger.info(
-    f"Added the following directory to PYTHONPATH to simulate multiple modules: {ROOT_DIR}"
-)
diff --git a/src/inference_pipeline/evaluation/evaluate.py b/src/inference_pipeline/evaluation/evaluate.py
deleted file mode 100644
index 084c793..0000000
--- a/src/inference_pipeline/evaluation/evaluate.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import argparse
-
-from config import settings
-from core.logger_utils import get_logger
-from core.opik_utils import create_dataset_from_artifacts
-from llm_twin import LLMTwin
-from opik.evaluation import evaluate
-from opik.evaluation.metrics import Hallucination, LevenshteinRatio, Moderation
-
-from .style import Style
-
-logger = get_logger(__name__)
-
-
-def evaluation_task(x: dict) -> dict:
-    inference_pipeline = LLMTwin(mock=False)
-    result = inference_pipeline.generate(
-        query=x["instruction"],
-        enable_rag=False,
-    )
-    answer = result["answer"]
-
-    return {
-        "input": x["instruction"],
-        "output": answer,
-        "expected_output": x["content"],
-        "reference": x["content"],
-    }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Evaluate monitoring script.")
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default="LLMTwinMonitoringDataset",
-        help="Name of the dataset to evaluate",
-    )
-
-    args = parser.parse_args()
-
-    dataset_name = args.dataset_name
-
-    logger.info(f"Evaluating Opik dataset: '{dataset_name}'")
-
-    dataset = create_dataset_from_artifacts(
-        dataset_name="LLMTwinArtifactTestDataset",
-        artifact_names=[
-            "articles-instruct-dataset",
-            "repositories-instruct-dataset",
-        ],
-    )
-    if dataset is None:
-        logger.error("Dataset can't be created. Exiting.")
-        exit(1)
-
-    experiment_config = {
-        "model_id": settings.MODEL_ID,
-    }
-    scoring_metrics = [
-        LevenshteinRatio(),
-        Hallucination(),
-        Moderation(),
-        Style(),
-    ]
-    evaluate(
-        dataset=dataset,
-        task=evaluation_task,
-        scoring_metrics=scoring_metrics,
-        experiment_config=experiment_config,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/inference_pipeline/evaluation/evaluate_monitoring.py b/src/inference_pipeline/evaluation/evaluate_monitoring.py
deleted file mode 100644
index 5000e7b..0000000
--- a/src/inference_pipeline/evaluation/evaluate_monitoring.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import argparse
-
-import opik
-from config import settings
-from core.logger_utils import get_logger
-from opik.evaluation import evaluate
-from opik.evaluation.metrics import AnswerRelevance, Hallucination, Moderation
-
-from .style import Style
-
-logger = get_logger(__name__)
-
-
-def evaluation_task(x: dict) -> dict:
-    return {
-        "input": x["input"]["query"],
-        "context": x["expected_output"]["context"],
-        "output": x["expected_output"]["answer"],
-    }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Evaluate monitoring script.")
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default="LLMTwinMonitoringDataset",
-        help="Name of the dataset to evaluate",
-    )
-
-    args = parser.parse_args()
-
-    dataset_name = args.dataset_name
-
-    logger.info(f"Evaluating Opik dataset: '{dataset_name}'")
-
-    client = opik.Opik()
-    try:
-        dataset = client.get_dataset(dataset_name)
-    except Exception:
-        logger.error(f"Monitoring dataset '{dataset_name}' not found in Opik. Exiting.")
-        exit(1)
-
-    experiment_config = {
-        "model_id": settings.MODEL_ID,
-    }
-
-    scoring_metrics = [Hallucination(), Moderation(), AnswerRelevance(), Style()]
-    evaluate(
-        dataset=dataset,
-        task=evaluation_task,
-        scoring_metrics=scoring_metrics,
-        experiment_config=experiment_config,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/inference_pipeline/evaluation/evaluate_rag.py b/src/inference_pipeline/evaluation/evaluate_rag.py
deleted file mode 100644
index aeb5c7a..0000000
--- a/src/inference_pipeline/evaluation/evaluate_rag.py
+++ /dev/null
@@ -1,86 +0,0 @@
-import argparse
-
-from core.config import settings
-from core.logger_utils import get_logger
-from core.opik_utils import create_dataset_from_artifacts
-from llm_twin import LLMTwin
-from opik.evaluation import evaluate
-from opik.evaluation.metrics import (
-    ContextPrecision,
-    ContextRecall,
-    Hallucination,
-)
-
-settings.patch_localhost()
-
-logger = get_logger(__name__)
-logger.warning(
-    "Patched settings to work with 'localhost' URLs. \
-    Remove the 'settings.patch_localhost()' call from above when deploying or running inside Docker."
-)
-
-
-def evaluation_task(x: dict) -> dict:
-    inference_pipeline = LLMTwin(mock=False)
-    result = inference_pipeline.generate(
-        query=x["instruction"],
-        enable_rag=True,
-    )
-    answer = result["answer"]
-    context = result["context"]
-
-    return {
-        "input": x["instruction"],
-        "output": answer,
-        "context": context,
-        "expected_output": x["content"],
-        "reference": x["content"],
-    }
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description="Evaluate monitoring script.")
-    parser.add_argument(
-        "--dataset_name",
-        type=str,
-        default="LLMTwinMonitoringDataset",
-        help="Name of the dataset to evaluate",
-    )
-
-    args = parser.parse_args()
-
-    dataset_name = args.dataset_name
-
-    logger.info(f"Evaluating Opik dataset: '{dataset_name}'")
-
-    dataset = create_dataset_from_artifacts(
-        dataset_name="LLMTwinArtifactTestDataset",
-        artifact_names=[
-            "articles-instruct-dataset",
-            "posts-instruct-dataset",
-            "repositories-instruct-dataset",
-        ],
-    )
-    if dataset is None:
-        logger.error("Dataset can't be created. Exiting.")
-        exit(1)
-
-    experiment_config = {
-        "model_id": settings.MODEL_ID,
-        "embedding_model_id": settings.EMBEDDING_MODEL_ID,
-    }
-    scoring_metrics = [
-        Hallucination(),
-        ContextRecall(),
-        ContextPrecision(),
-    ]
-    evaluate(
-        dataset=dataset,
-        task=evaluation_task,
-        scoring_metrics=scoring_metrics,
-        experiment_config=experiment_config,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/inference_pipeline/evaluation/style.py b/src/inference_pipeline/evaluation/style.py
deleted file mode 100644
index b7627d7..0000000
--- a/src/inference_pipeline/evaluation/style.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import json
-from typing import Any
-
-from config import settings
-from opik.evaluation.metrics import base_metric, exceptions, score_result
-from opik.evaluation.models import litellm_chat_model
-from pydantic import BaseModel
-
-
-class LLMJudgeStyleOutputResult(BaseModel):
-    score: int
-    reason: str
-
-
-class Style(base_metric.BaseMetric):
-    """
-    A metric that evaluates whether an LLM's output tone and writing style are appropriate for a blog post or social media content.
-
-    This metric uses another LLM to judge if the output is factual or contains hallucinations.
-    It returns a score of 1.0 if the style is appropriate, 0.5 if it is somewhere in the middle and 0.0 otherwise.
-    """
-
-    def __init__(
-        self, name: str = "style_metric", model_name: str = settings.OPENAI_MODEL_ID
-    ) -> None:
-        self.name = name
-        self.llm_client = litellm_chat_model.LiteLLMChatModel(model_name=model_name)
-        self.prompt_template = """
-        You are an impartial expert judge. Evaluate the quality of a given answer to an instruction based on it's style. 
-Style: Is the tone and writing style appropriate for a blog post or social media content? It should use simple but technical words and avoid formal or academic language.
-
-Style scale:
-1 (Poor): Too formal, uses some overly complex words
-2 (Good): Good balance of technical content and accessibility, but still uses formal words and expressions
-3 (Excellent): Perfectly accessible language for blog/social media, uses simple but precise technical terms when necessary
-
-Example of bad style: The Llama2 7B model constitutes a noteworthy progression in the field of artificial intelligence, serving as the successor to its predecessor, the original Llama architecture.
-Example of excellent style: Llama2 7B outperforms the original Llama model across multiple benchmarks.
-
-Instruction: {input}
-
-Answer: {output}
-
-Provide your evaluation in JSON format with the following structure:
-{{
-    "accuracy": {{
-        "reason": "...",
-        "score": 0
-    }},
-    "style": {{
-        "reason": "...",
-        "score": 0
-    }}
-}}
-"""
-
-    def score(self, input: str, output: str, **ignored_kwargs: Any):
-        """
-        Score the output of an LLM.
-
-        Args:
-            output: The output of an LLM to score.
-            **ignored_kwargs: Any additional keyword arguments. This is important so that the metric can be used in the `evaluate` function.
-        """
-
-        prompt = self.prompt_template.format(input=input, output=output)
-
-        model_output = self.llm_client.generate_string(
-            input=prompt, response_format=LLMJudgeStyleOutputResult
-        )
-
-        return self._parse_model_output(model_output)
-
-    def _parse_model_output(self, content: str) -> score_result.ScoreResult:
-        try:
-            dict_content = json.loads(content)
-        except Exception:
-            raise exceptions.MetricComputationError("Failed to parse the model output.")
-
-        score = dict_content["score"]
-        try:
-            assert 1 <= score <= 3, f"Invalid score value: {score}"
-        except AssertionError as e:
-            raise exceptions.MetricComputationError(str(e))
-
-        score = (score - 1) / 2.0  # Normalize the score to be between 0 and 1
-
-        return score_result.ScoreResult(
-            name=self.name,
-            value=score,
-            reason=dict_content["reason"],
-        )
diff --git a/src/inference_pipeline/llm_twin.py b/src/inference_pipeline/llm_twin.py
deleted file mode 100644
index 10de39e..0000000
--- a/src/inference_pipeline/llm_twin.py
+++ /dev/null
@@ -1,127 +0,0 @@
-import pprint
-
-import opik
-import sagemaker
-from config import settings
-from core import logger_utils
-from core.opik_utils import add_to_dataset_with_sampling
-from core.rag.retriever import VectorRetriever
-from langchain.prompts import PromptTemplate
-from opik import opik_context
-from prompt_templates import InferenceTemplate
-from sagemaker.huggingface.model import HuggingFacePredictor
-from utils import compute_num_tokens, truncate_text_to_max_tokens
-
-logger = logger_utils.get_logger(__name__)
-
-
-class LLMTwin:
-    def __init__(self, mock: bool = False) -> None:
-        self._mock = mock
-        self._llm_endpoint = self.build_sagemaker_predictor()
-        self.prompt_template_builder = InferenceTemplate()
-
-    def build_sagemaker_predictor(self) -> HuggingFacePredictor:
-        return HuggingFacePredictor(
-            endpoint_name=settings.DEPLOYMENT_ENDPOINT_NAME,
-            sagemaker_session=sagemaker.Session(),
-        )
-
-    @opik.track(name="inference_pipeline.generate")
-    def generate(
-        self,
-        query: str,
-        enable_rag: bool = False,
-        sample_for_evaluation: bool = False,
-    ) -> dict:
-        system_prompt, prompt_template = self.prompt_template_builder.create_template(
-            enable_rag=enable_rag
-        )
-        prompt_template_variables = {"question": query}
-
-        if enable_rag is True:
-            retriever = VectorRetriever(query=query)
-            hits = retriever.retrieve_top_k(
-                k=settings.TOP_K, to_expand_to_n_queries=settings.EXPAND_N_QUERY
-            )
-            context = retriever.rerank(hits=hits, keep_top_k=settings.KEEP_TOP_K)
-            prompt_template_variables["context"] = context
-        else:
-            context = None
-
-        messages, input_num_tokens = self.format_prompt(
-            system_prompt, prompt_template, prompt_template_variables
-        )
-
-        logger.debug(f"Prompt: {pprint.pformat(messages)}")
-        answer = self.call_llm_service(messages=messages)
-        logger.debug(f"Answer: {answer}")
-
-        num_answer_tokens = compute_num_tokens(answer)
-        opik_context.update_current_trace(
-            tags=["rag"],
-            metadata={
-                "prompt_template": prompt_template.template,
-                "prompt_template_variables": prompt_template_variables,
-                "model_id": settings.MODEL_ID,
-                "embedding_model_id": settings.EMBEDDING_MODEL_ID,
-                "input_tokens": input_num_tokens,
-                "answer_tokens": num_answer_tokens,
-                "total_tokens": input_num_tokens + num_answer_tokens,
-            },
-        )
-
-        answer = {"answer": answer, "context": context}
-        if sample_for_evaluation is True:
-            add_to_dataset_with_sampling(
-                item={"input": {"query": query}, "expected_output": answer},
-                dataset_name="LLMTwinMonitoringDataset",
-            )
-
-        return answer
-
-    @opik.track(name="inference_pipeline.format_prompt")
-    def format_prompt(
-        self,
-        system_prompt,
-        prompt_template: PromptTemplate,
-        prompt_template_variables: dict,
-    ) -> tuple[list[dict[str, str]], int]:
-        prompt = prompt_template.format(**prompt_template_variables)
-
-        num_system_prompt_tokens = compute_num_tokens(system_prompt)
-        prompt, prompt_num_tokens = truncate_text_to_max_tokens(
-            prompt, max_tokens=settings.MAX_INPUT_TOKENS - num_system_prompt_tokens
-        )
-        total_input_tokens = num_system_prompt_tokens + prompt_num_tokens
-
-        messages = [
-            {"role": "system", "content": system_prompt},
-            {"role": "user", "content": prompt},
-        ]
-
-        return messages, total_input_tokens
-
-    @opik.track(name="inference_pipeline.call_llm_service")
-    def call_llm_service(self, messages: list[dict[str, str]]) -> str:
-        if self._mock is True:
-            logger.warning("Mocking LLM service call.")
-
-            return "Mocked answer."
-
-        answer = self._llm_endpoint.predict(
-            data={
-                "messages": messages,
-                "parameters": {
-                    "max_new_tokens": settings.MAX_TOTAL_TOKENS
-                    - settings.MAX_INPUT_TOKENS,
-                    "temperature": 0.01,
-                    "top_p": 0.6,
-                    "stop": ["<|eot_id|>"],
-                    "return_full_text": False,
-                },
-            }
-        )
-        answer = answer["choices"][0]["message"]["content"].strip()
-
-        return answer
diff --git a/src/inference_pipeline/main.py b/src/inference_pipeline/main.py
deleted file mode 100644
index 0561240..0000000
--- a/src/inference_pipeline/main.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent)
-sys.path.append(ROOT_DIR)
-
-from core import logger_utils
-from core.config import settings
-from llm_twin import LLMTwin
-
-settings.patch_localhost()
-
-logger = logger_utils.get_logger(__name__)
-logger.info(
-    f"Added the following directory to PYTHONPATH to simulate multiple modules: {ROOT_DIR}"
-)
-logger.warning(
-    "Patched settings to work with 'localhost' URLs. \
-    Remove the 'settings.patch_localhost()' call from above when deploying or running inside Docker."
-)
-
-
-if __name__ == "__main__":
-    inference_endpoint = LLMTwin(mock=False)
-
-    query = """
-Hello I am Paul Iusztin.
-        
-Could you draft an article paragraph discussing RAG? 
-I'm particularly interested in how to design a RAG system.
-        """
-
-    response = inference_endpoint.generate(
-        query=query, enable_rag=True, sample_for_evaluation=True
-    )
-
-    logger.info("=" * 50)
-    logger.info(f"Query: {query}")
-    logger.info("=" * 50)
-    logger.info(f"Answer: {response['answer']}")
-    logger.info("=" * 50)
diff --git a/src/inference_pipeline/prompt_templates.py b/src/inference_pipeline/prompt_templates.py
deleted file mode 100644
index f849d4b..0000000
--- a/src/inference_pipeline/prompt_templates.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from core.rag.prompt_templates import BasePromptTemplate
-from langchain.prompts import PromptTemplate
-
-
-class InferenceTemplate(BasePromptTemplate):
-    simple_system_prompt: str = """
-    You are an AI language model assistant. Your task is to generate a cohesive and concise response based on the user's instruction by using a similar writing style and voice.
-"""
-    simple_prompt_template: str = """
-### Instruction:
-{question}
-"""
-
-    rag_system_prompt: str = """ You are a specialist in technical content writing. Your task is to create technical content based on the user's instruction given a specific context 
-with additional information consisting of the user's previous writings and his knowledge.
-
-Here is a list of steps that you need to follow in order to solve this task:
-
-Step 1: You need to analyze the user's instruction.
-Step 2: You need to analyze the provided context and how the information in it relates to the user instruction.
-Step 3: Generate the content keeping in mind that it needs to be as cohesive and concise as possible based on the query. You will use the users writing style and voice inferred from the user instruction and context.
-First try to answer based on the context. If the context is irrelevant answer with "I cannot answer your question, as I don't have enough context."
-"""
-    rag_prompt_template: str = """
-### Instruction:
-{question}
-
-### Context:
-{context}
-"""
-
-    def create_template(self, enable_rag: bool = True) -> tuple[str, PromptTemplate]:
-        if enable_rag is True:
-            return self.rag_system_prompt, PromptTemplate(
-                template=self.rag_prompt_template,
-                input_variables=["question", "context"],
-            )
-
-        return self.simple_system_prompt, PromptTemplate(
-            template=self.simple_prompt_template, input_variables=["question"]
-        )
diff --git a/src/inference_pipeline/ui.py b/src/inference_pipeline/ui.py
deleted file mode 100644
index 85e7e59..0000000
--- a/src/inference_pipeline/ui.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent)
-sys.path.append(ROOT_DIR)
-
-from core.config import settings
-from llm_twin import LLMTwin
-
-settings.patch_localhost()
-
-
-import gradio as gr
-from inference_pipeline.llm_twin import LLMTwin
-
-llm_twin = LLMTwin(mock=False)
-
-
-def predict(message: str, history: list[list[str]], author: str) -> str:
-    """
-    Generates a response using the LLM Twin, simulating a conversation with your digital twin.
-
-    Args:
-        message (str): The user's input message or question.
-        history (List[List[str]]): Previous conversation history between user and twin.
-        about_me (str): Personal context about the user to help personalize responses.
-
-    Returns:
-        str: The LLM Twin's generated response.
-    """
-
-    query = f"I am {author}. Write about: {message}"
-    response = llm_twin.generate(
-        query=query, enable_rag=True, sample_for_evaluation=False
-    )
-
-    return response["answer"]
-
-
-demo = gr.ChatInterface(
-    predict,
-    textbox=gr.Textbox(
-        placeholder="Chat with your LLM Twin",
-        label="Message",
-        container=False,
-        scale=7,
-    ),
-    additional_inputs=[
-        gr.Textbox(
-            "Paul Iusztin",
-            label="Who are you?",
-        )
-    ],
-    title="Your LLM Twin",
-    description="""
-    Chat with your personalized LLM Twin! This AI assistant will help you write content incorporating your style and voice.
-    """,
-    theme="soft",
-    examples=[
-        [
-            "Draft a post about RAG systems.",
-            "Paul Iusztin",
-        ],
-        [
-            "Draft an article paragraph about vector databases.",
-            "Paul Iusztin",
-        ],
-        [
-            "Draft a post about LLM chatbots.",
-            "Paul Iusztin",
-        ],
-    ],
-    cache_examples=False,
-)
-
-
-if __name__ == "__main__":
-    demo.queue().launch(server_name="0.0.0.0", server_port=7860, share=True)
diff --git a/src/inference_pipeline/utils.py b/src/inference_pipeline/utils.py
deleted file mode 100644
index bada6d2..0000000
--- a/src/inference_pipeline/utils.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from config import settings
-from transformers import AutoTokenizer
-
-
-def compute_num_tokens(text: str) -> int:
-    tokenizer = AutoTokenizer.from_pretrained(settings.MODEL_ID)
-
-    return len(tokenizer.encode(text, add_special_tokens=False))
-
-
-def truncate_text_to_max_tokens(text: str, max_tokens: int) -> tuple[str, int]:
-    """Truncates text to not exceed max_tokens while trying to preserve complete sentences.
-
-    Args:
-        text: The text to truncate
-        max_tokens: Maximum number of tokens allowed
-
-    Returns:
-        Truncated text that fits within max_tokens and the number of tokens in the truncated text.
-    """
-
-    current_tokens = compute_num_tokens(text)
-
-    if current_tokens <= max_tokens:
-        return text, current_tokens
-
-    tokenizer = AutoTokenizer.from_pretrained(settings.MODEL_ID)
-    tokens = tokenizer.encode(text, add_special_tokens=False)
-
-    # Take first max_tokens tokens and decode
-    truncated_tokens = tokens[:max_tokens]
-    truncated_text = tokenizer.decode(truncated_tokens)
-
-    # Try to end at last complete sentence
-    last_period = truncated_text.rfind(".")
-    if last_period > 0:
-        truncated_text = truncated_text[: last_period + 1]
-
-    truncated_tokens = compute_num_tokens(truncated_text)
-
-    return truncated_text, truncated_tokens
diff --git a/src/training_pipeline/config.py b/src/training_pipeline/config.py
deleted file mode 100644
index eb05c66..0000000
--- a/src/training_pipeline/config.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from pathlib import Path
-
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-ROOT_DIR = str(Path(__file__).parent.parent.parent.parent)
-
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=ROOT_DIR, env_file_encoding="utf-8")
-
-    # Hugging Face config
-    HUGGINGFACE_BASE_MODEL_ID: str = "meta-llama/Llama-3.1-8B"
-    HUGGINGFACE_ACCESS_TOKEN: str | None = None
-
-    # Comet config
-    COMET_API_KEY: str | None = None
-    COMET_WORKSPACE: str | None = None
-    COMET_PROJECT: str = "llm-twin"
-
-    DATASET_ID: str = "articles-instruct-dataset"  # Comet artifact containing your fine-tuning dataset (available after generating the instruct dataset).
-
-    # AWS config
-    AWS_REGION: str = "eu-central-1"
-    AWS_ACCESS_KEY: str | None = None
-    AWS_SECRET_KEY: str | None = None
-    AWS_ARN_ROLE: str | None = None
-
-
-settings = Settings()
diff --git a/src/training_pipeline/download_dataset.py b/src/training_pipeline/download_dataset.py
deleted file mode 100644
index eb4e22b..0000000
--- a/src/training_pipeline/download_dataset.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import json
-from pathlib import Path
-
-from comet_ml import Artifact, Experiment
-from comet_ml.artifacts import ArtifactAsset
-from config import settings
-from core import get_logger
-from datasets import Dataset  # noqa: E402
-
-logger = get_logger(__file__)
-
-
-class DatasetClient:
-    def __init__(
-        self,
-        output_dir: Path = Path("./finetuning_dataset"),
-    ) -> None:
-        self.output_dir = output_dir
-        self.output_dir.mkdir(parents=True, exist_ok=True)
-
-    def download_dataset(self, dataset_id: str, split: str = "train") -> Dataset:
-        assert split in ["train", "test"], "Split must be either 'train' or 'test'"
-
-        if "/" in dataset_id:
-            tokens = dataset_id.split("/")
-            assert (
-                len(tokens) == 2
-            ), f"Wrong format for the {dataset_id}. It should have a maximum one '/' character following the next template: 'comet_ml_workspace/comet_ml_artiface_name'"
-            workspace, artifact_name = tokens
-
-            experiment = Experiment(workspace=workspace)
-        else:
-            artifact_name = dataset_id
-
-            experiment = Experiment()
-
-        artifact = self._download_artifact(artifact_name, experiment)
-        asset = self._artifact_to_asset(artifact, split)
-        dataset = self._load_data(asset)
-
-        experiment.end()
-
-        return dataset
-
-    def _download_artifact(self, artifact_name: str, experiment) -> Artifact:
-        try:
-            logged_artifact = experiment.get_artifact(artifact_name)
-            artifact = logged_artifact.download(self.output_dir)
-        except Exception as e:
-            print(f"Error retrieving artifact: {str(e)}")
-
-            raise
-
-        print(f"Successfully downloaded  '{artifact_name}' at location '{self.output_dir}'")
-
-        return artifact
-
-    def _artifact_to_asset(self, artifact: Artifact, split: str) -> ArtifactAsset:
-        if len(artifact.assets) == 0:
-            raise RuntimeError("Artifact has no assets")
-        elif len(artifact.assets) != 2:
-            raise RuntimeError(
-                f"Artifact has more {len(artifact.assets)} assets, which is invalid. It should have only 2."
-            )
-
-        print(f"Picking split = '{split}'")
-        asset = [asset for asset in artifact.assets if split in asset.logical_path][0]
-
-        return asset
-
-    def _load_data(self, asset: ArtifactAsset) -> Dataset:
-        data_file_path = asset.local_path_or_data
-        with open(data_file_path, "r") as file:
-            data = json.load(file)
-
-        dataset_dict = {k: [str(d[k]) for d in data] for k in data[0].keys()}
-        dataset = Dataset.from_dict(dataset_dict)
-
-        print(
-            f"Successfully loaded dataset from artifact, num_samples = {len(dataset)}",
-        )
-
-        return dataset
-
-
-if __name__ == "__main__":
-    dataset_client = DatasetClient()
-    dataset_client.download_dataset(dataset_id=settings.DATASET_ID)
-
-    logger.info(f"Data available at '{dataset_client.output_dir}'.")
diff --git a/src/training_pipeline/finetune.py b/src/training_pipeline/finetune.py
deleted file mode 100644
index 157c4ec..0000000
--- a/src/training_pipeline/finetune.py
+++ /dev/null
@@ -1,328 +0,0 @@
-import argparse
-import json
-import os
-from pathlib import Path
-from typing import Any, List, Optional  # noqa: E402
-
-import torch  # noqa
-from comet_ml import Artifact, Experiment
-from comet_ml.artifacts import ArtifactAsset
-from datasets import Dataset, concatenate_datasets, load_dataset  # noqa: E402
-from transformers import TextStreamer, TrainingArguments  # noqa: E402
-from trl import SFTTrainer  # noqa: E402
-from unsloth import FastLanguageModel, is_bfloat16_supported  # noqa: E402
-from unsloth.chat_templates import get_chat_template  # noqa: E402
-
-ALPACA_TEMPLATE = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{}
-
-### Response:
-{}"""
-
-
-class DatasetClient:
-    def __init__(
-        self,
-        output_dir: Path = Path("./finetuning_dataset"),
-    ) -> None:
-        self.output_dir = output_dir
-        self.output_dir.mkdir(parents=True, exist_ok=True)
-
-    def download_dataset(self, dataset_id: str, split: str = "train") -> Dataset:
-        assert split in ["train", "test"], "Split must be either 'train' or 'test'"
-
-        if "/" in dataset_id:
-            tokens = dataset_id.split("/")
-            assert (
-                len(tokens) == 2
-            ), f"Wrong format for the {dataset_id}. It should have a maximum one '/' character following the next template: 'comet_ml_workspace/comet_ml_artiface_name'"
-            workspace, artifact_name = tokens
-
-            experiment = Experiment(workspace=workspace)
-        else:
-            artifact_name = dataset_id
-
-            experiment = Experiment()
-
-        artifact = self._download_artifact(artifact_name, experiment)
-        asset = self._artifact_to_asset(artifact, split)
-        dataset = self._load_data(asset)
-
-        experiment.end()
-
-        return dataset
-
-    def _download_artifact(self, artifact_name: str, experiment) -> Artifact:
-        try:
-            logged_artifact = experiment.get_artifact(artifact_name)
-            artifact = logged_artifact.download(self.output_dir)
-        except Exception as e:
-            print(f"Error retrieving artifact: {str(e)}")
-
-            raise
-
-        print(f"Successfully downloaded  {artifact_name} at location {self.output_dir}")
-
-        return artifact
-
-    def _artifact_to_asset(self, artifact: Artifact, split: str) -> ArtifactAsset:
-        if len(artifact.assets) == 0:
-            raise RuntimeError("Artifact has no assets")
-        elif len(artifact.assets) != 2:
-            raise RuntimeError(
-                f"Artifact has more {len(artifact.assets)} assets, which is invalid. It should have only 2."
-            )
-
-        print(f"Picking split = '{split}'")
-        asset = [asset for asset in artifact.assets if split in asset.logical_path][0]
-
-        return asset
-
-    def _load_data(self, asset: ArtifactAsset) -> Dataset:
-        data_file_path = asset.local_path_or_data
-        with open(data_file_path, "r") as file:
-            data = json.load(file)
-
-        dataset_dict = {k: [str(d[k]) for d in data] for k in data[0].keys()}
-        dataset = Dataset.from_dict(dataset_dict)
-
-        print(
-            f"Successfully loaded dataset from artifact, num_samples = {len(dataset)}",
-        )
-
-        return dataset
-
-
-def load_model(
-    model_name: str,
-    max_seq_length: int,
-    load_in_4bit: bool,
-    lora_rank: int,
-    lora_alpha: int,
-    lora_dropout: float,
-    target_modules: List[str],
-    chat_template: str,
-) -> tuple:
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name=model_name,
-        max_seq_length=max_seq_length,
-        load_in_4bit=load_in_4bit,
-    )
-
-    model = FastLanguageModel.get_peft_model(
-        model,
-        r=lora_rank,
-        lora_alpha=lora_alpha,
-        lora_dropout=lora_dropout,
-        target_modules=target_modules,
-    )
-
-    tokenizer = get_chat_template(
-        tokenizer,
-        chat_template=chat_template,
-    )
-
-    return model, tokenizer
-
-
-def finetune(
-    model_name: str,
-    output_dir: str,
-    dataset_id: str,
-    max_seq_length: int = 2048,
-    load_in_4bit: bool = False,
-    lora_rank: int = 32,
-    lora_alpha: int = 32,
-    lora_dropout: float = 0.0,
-    target_modules: List[str] = [
-        "q_proj",
-        "k_proj",
-        "v_proj",
-        "up_proj",
-        "down_proj",
-        "o_proj",
-        "gate_proj",
-    ],  # noqa: B006
-    chat_template: str = "chatml",
-    learning_rate: float = 3e-4,
-    num_train_epochs: int = 3,
-    per_device_train_batch_size: int = 2,
-    gradient_accumulation_steps: int = 8,
-    is_dummy: bool = True,
-) -> tuple:
-    model, tokenizer = load_model(
-        model_name,
-        max_seq_length,
-        load_in_4bit,
-        lora_rank,
-        lora_alpha,
-        lora_dropout,
-        target_modules,
-        chat_template,
-    )
-    EOS_TOKEN = tokenizer.eos_token
-    print(f"Setting EOS_TOKEN to {EOS_TOKEN}")  # noqa
-
-    if is_dummy is True:
-        num_train_epochs = 1
-        print(
-            f"Training in dummy mode. Setting num_train_epochs to '{num_train_epochs}'"
-        )  # noqa
-        print(f"Training in dummy mode. Reducing dataset size to '400'.")  # noqa
-
-    def format_samples_sft(examples):
-        text = []
-        for instruction, output in zip(
-            examples["instruction"], examples["content"], strict=False
-        ):
-            message = ALPACA_TEMPLATE.format(instruction, output) + EOS_TOKEN
-            text.append(message)
-
-        return {"text": text}
-
-    dataset_client = DatasetClient()
-    custom_dataset = dataset_client.download_dataset(dataset_id=dataset_id)
-    static_dataset = load_dataset(
-        "mlabonne/FineTome-Alpaca-100k", split="train[:10000]"
-    )
-    dataset = concatenate_datasets([custom_dataset, static_dataset])
-    if is_dummy:
-        dataset = dataset.select(range(400))
-    print(f"Loaded dataset with {len(dataset)} samples.")  # noqa
-
-    dataset = dataset.map(
-        format_samples_sft, batched=True, remove_columns=dataset.column_names
-    )
-    dataset = dataset.train_test_split(test_size=0.05)
-
-    print("Training dataset example:")  # noqa
-    print(dataset["train"][0])  # noqa
-
-    trainer = SFTTrainer(
-        model=model,
-        tokenizer=tokenizer,
-        train_dataset=dataset["train"],
-        eval_dataset=dataset["test"],
-        dataset_text_field="text",
-        max_seq_length=max_seq_length,
-        dataset_num_proc=2,
-        packing=True,
-        args=TrainingArguments(
-            learning_rate=learning_rate,
-            num_train_epochs=num_train_epochs,
-            per_device_train_batch_size=per_device_train_batch_size,
-            gradient_accumulation_steps=gradient_accumulation_steps,
-            fp16=not is_bfloat16_supported(),
-            bf16=is_bfloat16_supported(),
-            logging_steps=1,
-            optim="adamw_8bit",
-            weight_decay=0.01,
-            lr_scheduler_type="linear",
-            per_device_eval_batch_size=per_device_train_batch_size,
-            warmup_steps=10,
-            output_dir=output_dir,
-            report_to="comet_ml",
-            seed=0,
-        ),
-    )
-
-    trainer.train()
-
-    return model, tokenizer
-
-
-def inference(
-    model: Any,
-    tokenizer: Any,
-    prompt: str = "Write a paragraph to introduce supervised fine-tuning.",
-    max_new_tokens: int = 256,
-) -> None:
-    model = FastLanguageModel.for_inference(model)
-    message = ALPACA_TEMPLATE.format(prompt, "")
-    inputs = tokenizer([message], return_tensors="pt").to("cuda")
-
-    text_streamer = TextStreamer(tokenizer)
-    _ = model.generate(
-        **inputs, streamer=text_streamer, max_new_tokens=max_new_tokens, use_cache=True
-    )
-
-
-def save_model(
-    model: Any,
-    tokenizer: Any,
-    output_dir: str,
-    push_to_hub: bool = False,
-    repo_id: Optional[str] = None,
-) -> None:
-    model.save_pretrained_merged(output_dir, tokenizer, save_method="merged_16bit")
-
-    if push_to_hub and repo_id:
-        print(f"Saving model to '{repo_id}'")  # noqa
-        model.push_to_hub_merged(repo_id, tokenizer, save_method="merged_16bit")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--base_model_name", type=str, default="meta-llama/Llama-3.1-8B"
-    )
-    parser.add_argument("--dataset_id", type=str)
-    parser.add_argument("--num_train_epochs", type=int, default=3)
-    parser.add_argument("--per_device_train_batch_size", type=int, default=2)
-    parser.add_argument("--learning_rate", type=float, default=3e-4)
-    parser.add_argument("--model_output_huggingface_workspace", type=str)
-    parser.add_argument(
-        "--is_dummy",
-        type=bool,
-        default=False,
-        help="Flag to reduce the dataset size for testing",
-    )
-    parser.add_argument(
-        "--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"]
-    )
-    parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
-    parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])
-
-    args = parser.parse_args()
-
-    print(f"Num training epochs: '{args.num_train_epochs}'")  # noqa
-    print(f"Per device train batch size: '{args.per_device_train_batch_size}'")  # noqa
-    print(f"Learning rate: {args.learning_rate}")  # noqa
-    print(f"Datasets will be loaded from Comet ML artifact: '{args.dataset_id}'")  # noqa
-    print(
-        f"Models will be saved to Hugging Face workspace: '{args.model_output_huggingface_workspace}'"
-    )  # noqa
-    print(f"Training in dummy mode? '{args.is_dummy}'")  # noqa
-
-    print(f"Output data dir: '{args.output_data_dir}'")  # noqa
-    print(f"Model dir: '{args.model_dir}'")  # noqa
-    print(f"Number of GPUs: '{args.n_gpus}'")  # noqa
-
-    print("Starting SFT training...")  # noqa
-    print(f"Training from base model '{args.base_model_name}'")  # noqa
-
-    output_dir_sft = Path(args.model_dir) / "output_sft"
-    model, tokenizer = finetune(
-        model_name=args.base_model_name,
-        output_dir=str(output_dir_sft),
-        dataset_id=args.dataset_id,
-        num_train_epochs=args.num_train_epochs,
-        per_device_train_batch_size=args.per_device_train_batch_size,
-        learning_rate=args.learning_rate,
-    )
-    inference(model, tokenizer)
-
-    base_model_suffix = args.base_model_name.split("/")[-1]
-    sft_output_model_repo_id = (
-        f"{args.model_output_huggingface_workspace}/LLMTwin-{base_model_suffix}"
-    )
-    save_model(
-        model,
-        tokenizer,
-        "model_sft",
-        push_to_hub=True,
-        repo_id=sft_output_model_repo_id,
-    )
diff --git a/src/training_pipeline/requirements.txt b/src/training_pipeline/requirements.txt
deleted file mode 100644
index 18c436d..0000000
--- a/src/training_pipeline/requirements.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-accelerate==0.33.0
-torch==2.4.0
-transformers==4.43.3
-datasets==2.20.0
-peft==0.12.0
-trl==0.9.6
-bitsandbytes==0.43.3
-comet-ml==3.44.3
-flash-attn==2.3.6
-unsloth==2024.9.post2
diff --git a/src/training_pipeline/run_on_sagemaker.py b/src/training_pipeline/run_on_sagemaker.py
deleted file mode 100644
index 5e1eb75..0000000
--- a/src/training_pipeline/run_on_sagemaker.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import argparse
-import sys
-from pathlib import Path
-
-# To mimic using multiple Python modules, such as 'core' and 'feature_pipeline',
-# we will add the './src' directory to the PYTHONPATH. This is not intended for
-# production use cases but for development and educational purposes.
-ROOT_DIR = str(Path(__file__).parent.parent)
-sys.path.append(ROOT_DIR)
-
-from core import logger_utils
-from huggingface_hub import HfApi
-from sagemaker.huggingface import HuggingFace
-
-logger = logger_utils.get_logger(__file__)
-
-from config import settings
-
-finetuning_dir = Path(__file__).resolve().parent
-finetuning_requirements_path = finetuning_dir / "requirements.txt"
-
-
-def run_finetuning_on_sagemaker(
-    num_train_epochs: int = 3,
-    per_device_train_batch_size: int = 2,
-    learning_rate: float = 3e-4,
-    is_dummy: bool = False,
-) -> None:
-    assert settings.HUGGINGFACE_ACCESS_TOKEN, "Hugging Face access token (HUGGINGFACE_ACCESS_TOKEN) is required. Update your .env file."
-    assert (
-        settings.AWS_ARN_ROLE
-    ), "AWS ARN role (AWS_ARN_ROLE) is required. Update your .env file."
-    assert (
-        settings.COMET_API_KEY
-    ), "Comet ML API key (COMET_API_KEY) is required. Update your .env file."
-    assert (
-        settings.COMET_WORKSPACE
-    ), "Comet ML workspace (COMET_WORKSPACE) is required. Update your .env file."
-    assert (
-        settings.COMET_PROJECT
-    ), "Comet ML project name (COMET_PROJECT) is required. Update your .env file."
-
-    if not finetuning_dir.exists():
-        raise FileNotFoundError(f"The directory {finetuning_dir} does not exist.")
-    if not finetuning_requirements_path.exists():
-        raise FileNotFoundError(
-            f"The file {finetuning_requirements_path} does not exist."
-        )
-
-    api = HfApi()
-    user_info = api.whoami(token=settings.HUGGINGFACE_ACCESS_TOKEN)
-    huggingface_user = user_info["name"]
-    logger.info(f"Current Hugging Face user: {huggingface_user}")
-
-    hyperparameters = {
-        "base_model_name": settings.HUGGINGFACE_BASE_MODEL_ID,
-        "dataset_id": settings.DATASET_ID,
-        "num_train_epochs": num_train_epochs,
-        "per_device_train_batch_size": per_device_train_batch_size,
-        "learning_rate": learning_rate,
-        "model_output_huggingface_workspace": huggingface_user,
-    }
-    if is_dummy:
-        hyperparameters["is_dummy"] = True
-
-    # Create the HuggingFace SageMaker estimator
-    huggingface_estimator = HuggingFace(
-        entry_point="finetune.py",
-        source_dir=str(finetuning_dir),
-        instance_type="ml.g5.2xlarge",
-        instance_count=1,
-        role=settings.AWS_ARN_ROLE,
-        transformers_version="4.36",
-        pytorch_version="2.1",
-        py_version="py310",
-        hyperparameters=hyperparameters,
-        requirements_file=finetuning_requirements_path,
-        environment={
-            "HUGGING_FACE_HUB_TOKEN": settings.HUGGINGFACE_ACCESS_TOKEN,
-            "COMET_API_KEY": settings.COMET_API_KEY,
-            "COMET_WORKSPACE": settings.COMET_WORKSPACE,
-            "COMET_PROJECT_NAME": settings.COMET_PROJECT,
-        },
-    )
-
-    # Start the training job on SageMaker.
-    huggingface_estimator.fit()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--is-dummy", action="store_true", help="Run in dummy mode")
-    args = parser.parse_args()
-
-    logger.info(f"Is the training pipeline in DUMMY mode? '{args.is_dummy}'")
-
-    run_finetuning_on_sagemaker(is_dummy=args.is_dummy)
